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denoising</title> <meta name="description" content="Search results for: image denoising"> <meta name="keywords" content="image denoising"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="image denoising"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 2799</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: image denoising</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2799</span> Design and Implementation of Partial Denoising Boundary Image Matching Using Indexing Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bum-Soo%20Kim">Bum-Soo Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Jin-Uk%20Kim"> Jin-Uk Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we design and implement a partial denoising boundary image matching system using indexing techniques. Converting boundary images to time-series makes it feasible to perform fast search using indexes even on a very large image database. Thus, using this converting method we develop a client-server system based on the previous partial denoising research in the GUI (graphical user interface) environment. The client first converts a query image given by a user to a time-series and sends denoising parameters and the tolerance with this time-series to the server. The server identifies similar images from the index by evaluating a range query, which is constructed using inputs given from the client, and sends the resulting images to the client. Experimental results show that our system provides much intuitive and accurate matching result. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=boundary%20image%20matching" title="boundary image matching">boundary image matching</a>, <a href="https://publications.waset.org/abstracts/search?q=indexing" title=" indexing"> indexing</a>, <a href="https://publications.waset.org/abstracts/search?q=partial%20denoising" title=" partial denoising"> partial denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=time-series%20matching" title=" time-series matching"> time-series matching</a> </p> <a href="https://publications.waset.org/abstracts/97170/design-and-implementation-of-partial-denoising-boundary-image-matching-using-indexing-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97170.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">137</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2798</span> A Nonlocal Means Algorithm for Poisson Denoising Based on Information Geometry</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dongxu%20Chen">Dongxu Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Yipeng%20Li"> Yipeng Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an information geometry NonlocalMeans(NLM) algorithm for Poisson denoising. NLM estimates a noise-free pixel as a weighted average of image pixels, where each pixel is weighted according to the similarity between image patches in Euclidean space. In this work, every pixel is a Poisson distribution locally estimated by Maximum Likelihood (ML), all distributions consist of a statistical manifold. A NLM denoising algorithm is conducted on the statistical manifold where Fisher information matrix can be used for computing distribution geodesics referenced as the similarity between patches. This approach was demonstrated to be competitive with related state-of-the-art methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20denoising" title="image denoising">image denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=Poisson%20noise" title=" Poisson noise"> Poisson noise</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20geometry" title=" information geometry"> information geometry</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlocal-means" title=" nonlocal-means"> nonlocal-means</a> </p> <a href="https://publications.waset.org/abstracts/51221/a-nonlocal-means-algorithm-for-poisson-denoising-based-on-information-geometry" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51221.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">285</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2797</span> Noise Detection Algorithm for Skin Disease Image Identification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Minakshi%20Mainaji%20Sonawane">Minakshi Mainaji Sonawane</a>, <a href="https://publications.waset.org/abstracts/search?q=Bharti%20W.%20Gawali"> Bharti W. Gawali</a>, <a href="https://publications.waset.org/abstracts/search?q=Sudhir%20Mendhekar"> Sudhir Mendhekar</a>, <a href="https://publications.waset.org/abstracts/search?q=Ramesh%20R.%20Manza"> Ramesh R. Manza</a> </p> <p class="card-text"><strong>Abstract:</strong></p> People's lives and health are severely impacted by skin diseases. A new study proposes an effective method for identifying the different forms of skin diseases. Image denoising is a technique for improving image quality after it has been harmed by noise. The proposed technique is based on the usage of the wavelet transform. Wavelet transform is the best method for analyzing the image due to the ability to split the image into the sub-band, which has been used to estimate the noise ratio at the noisy image. According to experimental results, the proposed method presents the best values for MSE, PSNR, and Entropy for denoised images. we can found in Also, by using different types of wavelet transform filters is make the proposed approach can obtain the best results 23.13, 20.08, 50.7 for the image denoising process <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MSE" title="MSE">MSE</a>, <a href="https://publications.waset.org/abstracts/search?q=PSNR" title=" PSNR"> PSNR</a>, <a href="https://publications.waset.org/abstracts/search?q=entropy" title=" entropy"> entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20filter" title=" Gaussian filter"> Gaussian filter</a>, <a href="https://publications.waset.org/abstracts/search?q=DWT" title=" DWT"> DWT</a> </p> <a href="https://publications.waset.org/abstracts/142039/noise-detection-algorithm-for-skin-disease-image-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142039.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">215</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2796</span> Binarized-Weight Bilateral Filter for Low Computational Cost Image Smoothing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yu%20Zhang">Yu Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Kohei%20Inoue"> Kohei Inoue</a>, <a href="https://publications.waset.org/abstracts/search?q=Kiichi%20Urahama"> Kiichi Urahama</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We propose a simplified bilateral filter with binarized coefficients for accelerating it. Its computational cost is further decreased by sampling pixels. This computationally low cost filter is useful for smoothing or denoising images by using mobile devices with limited computational power. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bilateral%20filter" title="bilateral filter">bilateral filter</a>, <a href="https://publications.waset.org/abstracts/search?q=binarized-weight%20bilateral%20filter" title=" binarized-weight bilateral filter"> binarized-weight bilateral filter</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20smoothing" title=" image smoothing"> image smoothing</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20denoising" title=" image denoising"> image denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=pixel%20sampling" title=" pixel sampling"> pixel sampling</a> </p> <a href="https://publications.waset.org/abstracts/8980/binarized-weight-bilateral-filter-for-low-computational-cost-image-smoothing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8980.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">469</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2795</span> Hybrid Thresholding Lifting Dual Tree Complex Wavelet Transform with Wiener Filter for Quality Assurance of Medical Image</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hilal%20Naimi">Hilal Naimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Amelbahahouda%20Adamou-Mitiche"> Amelbahahouda Adamou-Mitiche</a>, <a href="https://publications.waset.org/abstracts/search?q=Lahcene%20Mitiche"> Lahcene Mitiche</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main problem in the area of medical imaging has been image denoising. The most defying for image denoising is to secure data carrying structures like surfaces and edges in order to achieve good visual quality. Different algorithms with different denoising performances have been proposed in previous decades. More recently, models focused on deep learning have shown a great promise to outperform all traditional approaches. However, these techniques are limited to the necessity of large sample size training and high computational costs. This research proposes a denoising approach basing on LDTCWT (Lifting Dual Tree Complex Wavelet Transform) using Hybrid Thresholding with Wiener filter to enhance the quality image. This research describes the LDTCWT as a type of lifting wavelets remodeling that produce complex coefficients by employing a dual tree of lifting wavelets filters to get its real part and imaginary part. Permits the remodel to produce approximate shift invariance, directionally selective filters and reduces the computation time (properties lacking within the classical wavelets transform). To develop this approach, a hybrid thresholding function is modeled by integrating the Wiener filter into the thresholding function. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lifting%20wavelet%20transform" title="lifting wavelet transform">lifting wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20denoising" title=" image denoising"> image denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=dual%20tree%20complex%20wavelet%20transform" title=" dual tree complex wavelet transform"> dual tree complex wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20shrinkage" title=" wavelet shrinkage"> wavelet shrinkage</a>, <a href="https://publications.waset.org/abstracts/search?q=wiener%20filter" title=" wiener filter"> wiener filter</a> </p> <a href="https://publications.waset.org/abstracts/135374/hybrid-thresholding-lifting-dual-tree-complex-wavelet-transform-with-wiener-filter-for-quality-assurance-of-medical-image" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135374.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">163</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2794</span> Automated Ultrasound Carotid Artery Image Segmentation Using Curvelet Threshold Decomposition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Latha%20Subbiah">Latha Subbiah</a>, <a href="https://publications.waset.org/abstracts/search?q=Dhanalakshmi%20Samiappan"> Dhanalakshmi Samiappan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose denoising Common Carotid Artery (CCA) B mode ultrasound images by a decomposition approach to curvelet thresholding and automatic segmentation of the intima media thickness and adventitia boundary. By decomposition, the local geometry of the image, its direction of gradients are well preserved. The components are combined into a single vector valued function, thus removes noise patches. Double threshold is applied to inherently remove speckle noise in the image. The denoised image is segmented by active contour without specifying seed points. Combined with level set theory, they provide sub regions with continuous boundaries. The deformable contours match to the shapes and motion of objects in the images. A curve or a surface under constraints is developed from the image with the goal that it is pulled into the necessary features of the image. Region based and boundary based information are integrated to achieve the contour. The method treats the multiplicative speckle noise in objective and subjective quality measurements and thus leads to better-segmented results. The proposed denoising method gives better performance metrics compared with other state of art denoising algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=curvelet" title="curvelet">curvelet</a>, <a href="https://publications.waset.org/abstracts/search?q=decomposition" title=" decomposition"> decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=levelset" title=" levelset"> levelset</a>, <a href="https://publications.waset.org/abstracts/search?q=ultrasound" title=" ultrasound"> ultrasound</a> </p> <a href="https://publications.waset.org/abstracts/56351/automated-ultrasound-carotid-artery-image-segmentation-using-curvelet-threshold-decomposition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56351.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">340</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2793</span> Improved Super-Resolution Using Deep Denoising Convolutional Neural Network </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pawan%20Kumar%20Mishra">Pawan Kumar Mishra</a>, <a href="https://publications.waset.org/abstracts/search?q=Ganesh%20Singh%20Bisht"> Ganesh Singh Bisht</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Super-resolution is the technique that is being used in computer vision to construct high-resolution images from a single low-resolution image. It is used to increase the frequency component, recover the lost details and removing the down sampling and noises that caused by camera during image acquisition process. High-resolution images or videos are desired part of all image processing tasks and its analysis in most of digital imaging application. The target behind super-resolution is to combine non-repetition information inside single or multiple low-resolution frames to generate a high-resolution image. Many methods have been proposed where multiple images are used as low-resolution images of same scene with different variation in transformation. This is called multi-image super resolution. And another family of methods is single image super-resolution that tries to learn redundancy that presents in image and reconstruction the lost information from a single low-resolution image. Use of deep learning is one of state of art method at present for solving reconstruction high-resolution image. In this research, we proposed Deep Denoising Super Resolution (DDSR) that is a deep neural network for effectively reconstruct the high-resolution image from low-resolution image. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=resolution" title="resolution">resolution</a>, <a href="https://publications.waset.org/abstracts/search?q=deep-learning" title=" deep-learning"> deep-learning</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=de-blurring" title=" de-blurring"> de-blurring</a> </p> <a href="https://publications.waset.org/abstracts/78802/improved-super-resolution-using-deep-denoising-convolutional-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78802.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">517</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2792</span> The Influence of Noise on Aerial Image Semantic Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pengchao%20Wei">Pengchao Wei</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiangzhong%20Fang"> Xiangzhong Fang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Noise is ubiquitous in this world. Denoising is an essential technology, especially in image semantic segmentation, where noises are generally categorized into two main types i.e. feature noise and label noise. The main focus of this paper is aiming at modeling label noise, investigating the behaviors of different types of label noise on image semantic segmentation tasks using K-Nearest-Neighbor and Convolutional Neural Network classifier. The performance without label noise and with is evaluated and illustrated in this paper. In addition to that, the influence of feature noise on the image semantic segmentation task is researched as well and a feature noise reduction method is applied to mitigate its influence in the learning procedure. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title="convolutional neural network">convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=denoising" title=" denoising"> denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20noise" title=" feature noise"> feature noise</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20semantic%20segmentation" title=" image semantic segmentation"> image semantic segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=k-nearest-neighbor" title=" k-nearest-neighbor"> k-nearest-neighbor</a>, <a href="https://publications.waset.org/abstracts/search?q=label%20noise" title=" label noise"> label noise</a> </p> <a href="https://publications.waset.org/abstracts/141479/the-influence-of-noise-on-aerial-image-semantic-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141479.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">220</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2791</span> An Image Enhancement Method Based on Curvelet Transform for CBCT-Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shahriar%20Farzam">Shahriar Farzam</a>, <a href="https://publications.waset.org/abstracts/search?q=Maryam%20Rastgarpour"> Maryam Rastgarpour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image denoising plays extremely important role in digital image processing. Enhancement of clinical image research based on Curvelet has been developed rapidly in recent years. In this paper, we present a method for image contrast enhancement for cone beam CT (CBCT) images based on fast discrete curvelet transforms (FDCT) that work through Unequally Spaced Fast Fourier Transform (USFFT). These transforms return a table of Curvelet transform coefficients indexed by a scale parameter, an orientation and a spatial location. Accordingly, the coefficients obtained from FDCT-USFFT can be modified in order to enhance contrast in an image. Our proposed method first uses a two-dimensional mathematical transform, namely the FDCT through unequal-space fast Fourier transform on input image and then applies thresholding on coefficients of Curvelet to enhance the CBCT images. Consequently, applying unequal-space fast Fourier Transform leads to an accurate reconstruction of the image with high resolution. The experimental results indicate the performance of the proposed method is superior to the existing ones in terms of Peak Signal to Noise Ratio (PSNR) and Effective Measure of Enhancement (EME). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=curvelet%20transform" title="curvelet transform">curvelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=CBCT" title=" CBCT"> CBCT</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20enhancement" title=" image enhancement"> image enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20denoising" title=" image denoising"> image denoising</a> </p> <a href="https://publications.waset.org/abstracts/69244/an-image-enhancement-method-based-on-curvelet-transform-for-cbct-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69244.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">300</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2790</span> A Nonlinear Parabolic Partial Differential Equation Model for Image Enhancement</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tudor%20Barbu">Tudor Barbu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We present a robust nonlinear parabolic partial differential equation (PDE)-based denoising scheme in this article. Our approach is based on a second-order anisotropic diffusion model that is described first. Then, a consistent and explicit numerical approximation algorithm is constructed for this continuous model by using the finite-difference method. Finally, our restoration experiments and method comparison, which prove the effectiveness of this proposed technique, are discussed in this paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=anisotropic%20diffusion" title="anisotropic diffusion">anisotropic diffusion</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20differences" title=" finite differences"> finite differences</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20denoising%20and%20restoration" title=" image denoising and restoration"> image denoising and restoration</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20PDE%20model" title=" nonlinear PDE model"> nonlinear PDE model</a>, <a href="https://publications.waset.org/abstracts/search?q=anisotropic%20diffusion" title=" anisotropic diffusion"> anisotropic diffusion</a>, <a href="https://publications.waset.org/abstracts/search?q=numerical%20approximation%20schemes" title=" numerical approximation schemes"> numerical approximation schemes</a> </p> <a href="https://publications.waset.org/abstracts/48289/a-nonlinear-parabolic-partial-differential-equation-model-for-image-enhancement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48289.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">312</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2789</span> The Clustering of Multiple Sclerosis Subgroups through L2 Norm Multifractal Denoising Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yeliz%20Karaca">Yeliz Karaca</a>, <a href="https://publications.waset.org/abstracts/search?q=Rana%20Karabudak"> Rana Karabudak</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multifractal Denoising techniques are used in the identification of significant attributes by removing the noise of the dataset. Magnetic resonance (MR) image technique is the most sensitive method so as to identify chronic disorders of the nervous system such as Multiple Sclerosis. MRI and Expanded Disability Status Scale (EDSS) data belonging to 120 individuals who have one of the subgroups of MS (Relapsing Remitting MS (RRMS), Secondary Progressive MS (SPMS), Primary Progressive MS (PPMS)) as well as 19 healthy individuals in the control group have been used in this study. The study is comprised of the following stages: (i) L2 Norm Multifractal Denoising technique, one of the multifractal technique, has been used with the application on the MS data (MRI and EDSS). In this way, the new dataset has been obtained. (ii) The new MS dataset obtained from the MS dataset and L2 Multifractal Denoising technique has been applied to the K-Means and Fuzzy C Means clustering algorithms which are among the unsupervised methods. Thus, the clustering performances have been compared. (iii) In the identification of significant attributes in the MS dataset through the Multifractal denoising (L2 Norm) technique using K-Means and FCM algorithms on the MS subgroups and control group of healthy individuals, excellent performance outcome has been yielded. According to the clustering results based on the MS subgroups obtained in the study, successful clustering results have been obtained in the K-Means and FCM algorithms by applying the L2 norm of multifractal denoising technique for the MS dataset. Clustering performance has been more successful with the MS Dataset (L2_Norm MS Data Set) K-Means and FCM in which significant attributes are obtained by applying L2 Norm Denoising technique. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clinical%20decision%20support" title="clinical decision support">clinical decision support</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering%20algorithms" title=" clustering algorithms"> clustering algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20sclerosis" title=" multiple sclerosis"> multiple sclerosis</a>, <a href="https://publications.waset.org/abstracts/search?q=multifractal%20techniques" title=" multifractal techniques"> multifractal techniques</a> </p> <a href="https://publications.waset.org/abstracts/91074/the-clustering-of-multiple-sclerosis-subgroups-through-l2-norm-multifractal-denoising-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/91074.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">168</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2788</span> Electrocardiogram Signal Denoising Using a Hybrid Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Latif">R. Latif</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20Jenkal"> W. Jenkal</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Toumanari"> A. Toumanari</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Hatim"> A. Hatim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an efficient method of electrocardiogram signal denoising based on a hybrid approach. Two techniques are brought together to create an efficient denoising process. The first is an Adaptive Dual Threshold Filter (ADTF) and the second is the Discrete Wavelet Transform (DWT). The presented approach is based on three steps of denoising, the DWT decomposition, the ADTF step and the highest peaks correction step. This paper presents some application of the approach on some electrocardiogram signals of the MIT-BIH database. The results of these applications are promising compared to other recently published techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hybrid%20technique" title="hybrid technique">hybrid technique</a>, <a href="https://publications.waset.org/abstracts/search?q=ADTF" title=" ADTF"> ADTF</a>, <a href="https://publications.waset.org/abstracts/search?q=DWT" title=" DWT"> DWT</a>, <a href="https://publications.waset.org/abstracts/search?q=thresholding" title=" thresholding"> thresholding</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG%20signal" title=" ECG signal"> ECG signal</a> </p> <a href="https://publications.waset.org/abstracts/65458/electrocardiogram-signal-denoising-using-a-hybrid-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65458.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">322</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2787</span> Multi-Scaled Non-Local Means Filter for Medical Images Denoising: Empirical Mode Decomposition vs. Wavelet Transform </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hana%20Rabbouch">Hana Rabbouch</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, there has been considerable growth of denoising techniques mainly devoted to medical imaging. This important evolution is not only due to the progress of computing techniques, but also to the emergence of multi-resolution analysis (MRA) on both mathematical and algorithmic bases. In this paper, a comparative study is conducted between the two best-known MRA-based decomposition techniques: the Empirical Mode Decomposition (EMD) and the Discrete Wavelet Transform (DWT). The comparison is carried out in a framework of multi-scale denoising, where a Non-Local Means (NLM) filter is performed scale-by-scale to a sample of benchmark medical images. The results prove the effectiveness of the multiscaled denoising, especially when the NLM filtering is coupled with the EMD. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=medical%20imaging" title="medical imaging">medical imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=non%20local%20means" title=" non local means"> non local means</a>, <a href="https://publications.waset.org/abstracts/search?q=denoising" title=" denoising"> denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=multiscaled%20analysis" title=" multiscaled analysis"> multiscaled analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=empirical%20mode%20decomposition" title=" empirical mode decomposition"> empirical mode decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelets" title=" wavelets"> wavelets</a> </p> <a href="https://publications.waset.org/abstracts/115243/multi-scaled-non-local-means-filter-for-medical-images-denoising-empirical-mode-decomposition-vs-wavelet-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/115243.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">141</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2786</span> Denoising of Magnetotelluric Signals by Filtering </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rodrigo%20Montufar-Chaveznava">Rodrigo Montufar-Chaveznava</a>, <a href="https://publications.waset.org/abstracts/search?q=Fernando%20Brambila-Paz"> Fernando Brambila-Paz</a>, <a href="https://publications.waset.org/abstracts/search?q=Ivette%20Caldelas"> Ivette Caldelas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present the advances corresponding to the denoising processing of magnetotelluric signals using several filters. In particular, we use the most common spatial domain filters such as median and mean, but we are also using the Fourier and wavelet transform for frequency domain filtering. We employ three datasets obtained at the different sampling rate (128, 4096 and 8192 bps) and evaluate the mean square error, signal-to-noise relation, and peak signal-to-noise relation to compare the kernels and determine the most suitable for each case. The magnetotelluric signals correspond to earth exploration when water is searched. The object is to find a denoising strategy different to the one included in the commercial equipment that is employed in this task. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=denoising" title="denoising">denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=filtering" title=" filtering"> filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetotelluric%20signals" title=" magnetotelluric signals"> magnetotelluric signals</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20transform" title=" wavelet transform"> wavelet transform</a> </p> <a href="https://publications.waset.org/abstracts/91383/denoising-of-magnetotelluric-signals-by-filtering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/91383.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">370</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2785</span> Empirical Mode Decomposition Based Denoising by Customized Thresholding</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wahiba%20Mohguen">Wahiba Mohguen</a>, <a href="https://publications.waset.org/abstracts/search?q=Ra%C3%AFs%20El%E2%80%99hadi%20Bekka"> Ra茂s El鈥檋adi Bekka</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a denoising method called EMD-Custom that was based on Empirical Mode Decomposition (EMD) and the modified Customized Thresholding Function (Custom) algorithms. EMD was applied to decompose adaptively a noisy signal into intrinsic mode functions (IMFs). Then, all the noisy IMFs got threshold by applying the presented thresholding function to suppress noise and to improve the signal to noise ratio (SNR). The method was tested on simulated data and real ECG signal, and the results were compared to the EMD-Based signal denoising methods using the soft and hard thresholding. The results showed the superior performance of the proposed EMD-Custom denoising over the traditional approach. The performances were evaluated in terms of SNR in dB, and Mean Square Error (MSE). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=customized%20thresholding" title="customized thresholding">customized thresholding</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG%20signal" title=" ECG signal"> ECG signal</a>, <a href="https://publications.waset.org/abstracts/search?q=EMD" title=" EMD"> EMD</a>, <a href="https://publications.waset.org/abstracts/search?q=hard%20thresholding" title=" hard thresholding"> hard thresholding</a>, <a href="https://publications.waset.org/abstracts/search?q=soft-thresholding" title=" soft-thresholding"> soft-thresholding</a> </p> <a href="https://publications.waset.org/abstracts/67421/empirical-mode-decomposition-based-denoising-by-customized-thresholding" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67421.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">302</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2784</span> Optimizing Machine Learning Through Python Based Image Processing Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Srinidhi.%20A">Srinidhi. A</a>, <a href="https://publications.waset.org/abstracts/search?q=Naveed%20Ahmed"> Naveed Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Twinkle%20Hareendran"> Twinkle Hareendran</a>, <a href="https://publications.waset.org/abstracts/search?q=Vriksha%20Prakash"> Vriksha Prakash</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work reviews some of the advanced image processing techniques for deep learning applications. Object detection by template matching, image denoising, edge detection, and super-resolution modelling are but a few of the tasks. The paper looks in into great detail, given that such tasks are crucial preprocessing steps that increase the quality and usability of image datasets in subsequent deep learning tasks. We review some of the methods for the assessment of image quality, more specifically sharpness, which is crucial to ensure a robust performance of models. Further, we will discuss the development of deep learning models specific to facial emotion detection, age classification, and gender classification, which essentially includes the preprocessing techniques interrelated with model performance. Conclusions from this study pinpoint the best practices in the preparation of image datasets, targeting the best trade-off between computational efficiency and retaining important image features critical for effective training of deep learning models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title="image processing">image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning%20applications" title=" machine learning applications"> machine learning applications</a>, <a href="https://publications.waset.org/abstracts/search?q=template%20matching" title=" template matching"> template matching</a>, <a href="https://publications.waset.org/abstracts/search?q=emotion%20detection" title=" emotion detection"> emotion detection</a> </p> <a href="https://publications.waset.org/abstracts/193107/optimizing-machine-learning-through-python-based-image-processing-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193107.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">13</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2783</span> Breast Cancer Metastasis Detection and Localization through Transfer-Learning Convolutional Neural Network Classification Based on Convolutional Denoising Autoencoder Stack</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Varun%20Agarwal">Varun Agarwal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: With the advent of personalized medicine, histopathological review of whole slide images (WSIs) for cancer diagnosis presents an exceedingly time-consuming, complex task. Specifically, detecting metastatic regions in WSIs of sentinel lymph node biopsies necessitates a full-scanned, holistic evaluation of the image. Thus, digital pathology, low-level image manipulation algorithms, and machine learning provide significant advancements in improving the efficiency and accuracy of WSI analysis. Using Camelyon16 data, this paper proposes a deep learning pipeline to automate and ameliorate breast cancer metastasis localization and WSI classification. Methodology: The model broadly follows five stages -region of interest detection, WSI partitioning into image tiles, convolutional neural network (CNN) image-segment classifications, probabilistic mapping of tumor localizations, and further processing for whole WSI classification. Transfer learning is applied to the task, with the implementation of Inception-ResNetV2 - an effective CNN classifier that uses residual connections to enhance feature representation, adding convolved outputs in the inception unit to the proceeding input data. Moreover, in order to augment the performance of the transfer learning CNN, a stack of convolutional denoising autoencoders (CDAE) is applied to produce embeddings that enrich image representation. Through a saliency-detection algorithm, visual training segments are generated, which are then processed through a denoising autoencoder -primarily consisting of convolutional, leaky rectified linear unit, and batch normalization layers- and subsequently a contrast-normalization function. A spatial pyramid pooling algorithm extracts the key features from the processed image, creating a viable feature map for the CNN that minimizes spatial resolution and noise. Results and Conclusion: The simplified and effective architecture of the fine-tuned transfer learning Inception-ResNetV2 network enhanced with the CDAE stack yields state of the art performance in WSI classification and tumor localization, achieving AUC scores of 0.947 and 0.753, respectively. The convolutional feature retention and compilation with the residual connections to inception units synergized with the input denoising algorithm enable the pipeline to serve as an effective, efficient tool in the histopathological review of WSIs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title="breast cancer">breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title=" convolutional neural networks"> convolutional neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=metastasis%20mapping" title=" metastasis mapping"> metastasis mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=whole%20slide%20images" title=" whole slide images"> whole slide images</a> </p> <a href="https://publications.waset.org/abstracts/133783/breast-cancer-metastasis-detection-and-localization-through-transfer-learning-convolutional-neural-network-classification-based-on-convolutional-denoising-autoencoder-stack" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133783.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">130</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2782</span> Sparsity Order Selection and Denoising in Compressed Sensing Framework</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahdi%20Shamsi">Mahdi Shamsi</a>, <a href="https://publications.waset.org/abstracts/search?q=Tohid%20Yousefi%20Rezaii"> Tohid Yousefi Rezaii</a>, <a href="https://publications.waset.org/abstracts/search?q=Siavash%20Eftekharifar"> Siavash Eftekharifar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Compressed sensing (CS) is a new powerful mathematical theory concentrating on sparse signals which is widely used in signal processing. The main idea is to sense sparse signals by far fewer measurements than the Nyquist sampling rate, but the reconstruction process becomes nonlinear and more complicated. Common dilemma in sparse signal recovery in CS is the lack of knowledge about sparsity order of the signal, which can be viewed as model order selection procedure. In this paper, we address the problem of sparsity order estimation in sparse signal recovery. This is of main interest in situations where the signal sparsity is unknown or the signal to be recovered is approximately sparse. It is shown that the proposed method also leads to some kind of signal denoising, where the observations are contaminated with noise. Finally, the performance of the proposed approach is evaluated in different scenarios and compared to an existing method, which shows the effectiveness of the proposed method in terms of order selection as well as denoising. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=compressed%20sensing" title="compressed sensing">compressed sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20denoising" title=" data denoising"> data denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20order%20selection" title=" model order selection"> model order selection</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20representation" title=" sparse representation"> sparse representation</a> </p> <a href="https://publications.waset.org/abstracts/31470/sparsity-order-selection-and-denoising-in-compressed-sensing-framework" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31470.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">483</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2781</span> Improvement of Bone Scintography Image Using Image Texture Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yousif%20Mohamed%20Y.%20Abdallah">Yousif Mohamed Y. Abdallah</a>, <a href="https://publications.waset.org/abstracts/search?q=Eltayeb%20Wagallah"> Eltayeb Wagallah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image enhancement allows the observer to see details in images that may not be immediately observable in the original image. Image enhancement is the transformation or mapping of one image to another. The enhancement of certain features in images is accompanied by undesirable effects. To achieve maximum image quality after denoising, a new, low order, local adaptive Gaussian scale mixture model and median filter were presented, which accomplishes nonlinearities from scattering a new nonlinear approach for contrast enhancement of bones in bone scan images using both gamma correction and negative transform methods. The usual assumption of a distribution of gamma and Poisson statistics only lead to overestimation of the noise variance in regions of low intensity but to underestimation in regions of high intensity and therefore to non-optional results. The contrast enhancement results were obtained and evaluated using MatLab program in nuclear medicine images of the bones. The optimal number of bins, in particular the number of gray-levels, is chosen automatically using entropy and average distance between the histogram of the original gray-level distribution and the contrast enhancement function鈥檚 curve. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bone%20scan" title="bone scan">bone scan</a>, <a href="https://publications.waset.org/abstracts/search?q=nuclear%20medicine" title=" nuclear medicine"> nuclear medicine</a>, <a href="https://publications.waset.org/abstracts/search?q=Matlab" title=" Matlab"> Matlab</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing%20technique" title=" image processing technique"> image processing technique</a> </p> <a href="https://publications.waset.org/abstracts/13956/improvement-of-bone-scintography-image-using-image-texture-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13956.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">506</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2780</span> Wavelet Coefficients Based on Orthogonal Matching Pursuit (OMP) Based Filtering for Remotely Sensed Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ramandeep%20Kaur">Ramandeep Kaur</a>, <a href="https://publications.waset.org/abstracts/search?q=Kamaljit%20Kaur"> Kamaljit Kaur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, the technology of the remote sensing is growing rapidly. Image enhancement is one of most commonly used of image processing operations. Noise reduction plays very important role in digital image processing and various technologies have been located ahead to reduce the noise of the remote sensing images. The noise reduction using wavelet coefficients based on Orthogonal Matching Pursuit (OMP) has less consequences on the edges than available methods but this is not as establish in edge preservation techniques. So in this paper we provide a new technique minimum patch based noise reduction OMP which reduce the noise from an image and used edge preservation patch which preserve the edges of the image and presents the superior results than existing OMP technique. Experimental results show that the proposed minimum patch approach outperforms over existing techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20denoising" title="image denoising">image denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=minimum%20patch" title=" minimum patch"> minimum patch</a>, <a href="https://publications.waset.org/abstracts/search?q=OMP" title=" OMP"> OMP</a>, <a href="https://publications.waset.org/abstracts/search?q=WCOMP" title=" WCOMP"> WCOMP</a> </p> <a href="https://publications.waset.org/abstracts/59831/wavelet-coefficients-based-on-orthogonal-matching-pursuit-omp-based-filtering-for-remotely-sensed-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59831.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">389</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2779</span> Speckle Noise Reduction Using Anisotropic Filter Based on Wavelets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kritika%20Bansal">Kritika Bansal</a>, <a href="https://publications.waset.org/abstracts/search?q=Akwinder%20Kaur"> Akwinder Kaur</a>, <a href="https://publications.waset.org/abstracts/search?q=Shruti%20Gujral"> Shruti Gujral</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the approach of denoising is solved by using a new hybrid technique which associates the different denoising methods. Wavelet thresholding and anisotropic diffusion filter are the two different filters in our hybrid techniques. The Wavelet thresholding removes the noise by removing the high frequency components with lesser edge preservation, whereas an anisotropic diffusion filters is based on partial differential equation, (PDE) to remove the speckle noise. This PDE approach is used to preserve the edges and provides better smoothing. So our new method proposes a combination of these two filtering methods which performs better results in terms of peak signal to noise ratio (PSNR), coefficient of correlation (COC) and equivalent no of looks (ENL). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=denoising" title="denoising">denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=anisotropic%20diffusion%20filter" title=" anisotropic diffusion filter"> anisotropic diffusion filter</a>, <a href="https://publications.waset.org/abstracts/search?q=multiplicative%20noise" title=" multiplicative noise"> multiplicative noise</a>, <a href="https://publications.waset.org/abstracts/search?q=speckle" title=" speckle"> speckle</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelets" title=" wavelets"> wavelets</a> </p> <a href="https://publications.waset.org/abstracts/14626/speckle-noise-reduction-using-anisotropic-filter-based-on-wavelets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14626.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">512</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2778</span> A Sparse Representation Speech Denoising Method Based on Adapted Stopping Residue Error</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Qianhua%20He">Qianhua He</a>, <a href="https://publications.waset.org/abstracts/search?q=Weili%20Zhou"> Weili Zhou</a>, <a href="https://publications.waset.org/abstracts/search?q=Aiwu%20Chen"> Aiwu Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A sparse representation speech denoising method based on adapted stopping residue error was presented in this paper. Firstly, the cross-correlation between the clean speech spectrum and the noise spectrum was analyzed, and an estimation method was proposed. In the denoising method, an over-complete dictionary of the clean speech power spectrum was learned with the K-singular value decomposition (K-SVD) algorithm. In the sparse representation stage, the stopping residue error was adaptively achieved according to the estimated cross-correlation and the adjusted noise spectrum, and the orthogonal matching pursuit (OMP) approach was applied to reconstruct the clean speech spectrum from the noisy speech. Finally, the clean speech was re-synthesised via the inverse Fourier transform with the reconstructed speech spectrum and the noisy speech phase. The experiment results show that the proposed method outperforms the conventional methods in terms of subjective and objective measure. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=speech%20denoising" title="speech denoising">speech denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20representation" title=" sparse representation"> sparse representation</a>, <a href="https://publications.waset.org/abstracts/search?q=k-singular%20value%20decomposition" title=" k-singular value decomposition"> k-singular value decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=orthogonal%20matching%20pursuit" title=" orthogonal matching pursuit"> orthogonal matching pursuit</a> </p> <a href="https://publications.waset.org/abstracts/66670/a-sparse-representation-speech-denoising-method-based-on-adapted-stopping-residue-error" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66670.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">499</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2777</span> CT Medical Images Denoising Based on New Wavelet Thresholding Compared with Curvelet and Contourlet</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amir%20Moslemi">Amir Moslemi</a>, <a href="https://publications.waset.org/abstracts/search?q=Amir%20movafeghi"> Amir movafeghi</a>, <a href="https://publications.waset.org/abstracts/search?q=Shahab%20Moradi"> Shahab Moradi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the most important challenging factors in medical images is nominated as noise.Image denoising refers to the improvement of a digital medical image that has been infected by Additive White Gaussian Noise (AWGN). The digital medical image or video can be affected by different types of noises. They are impulse noise, Poisson noise and AWGN. Computed tomography (CT) images are subjected to low quality due to the noise. The quality of CT images is dependent on the absorbed dose to patients directly in such a way that increase in absorbed radiation, consequently absorbed dose to patients (ADP), enhances the CT images quality. In this manner, noise reduction techniques on the purpose of images quality enhancement exposing no excess radiation to patients is one the challenging problems for CT images processing. In this work, noise reduction in CT images was performed using two different directional 2 dimensional (2D) transformations; i.e., Curvelet and Contourlet and Discrete wavelet transform(DWT) thresholding methods of BayesShrink and AdaptShrink, compared to each other and we proposed a new threshold in wavelet domain for not only noise reduction but also edge retaining, consequently the proposed method retains the modified coefficients significantly that result in good visual quality. Data evaluations were accomplished by using two criterions; namely, peak signal to noise ratio (PSNR) and Structure similarity (Ssim). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computed%20tomography%20%28CT%29" title="computed tomography (CT)">computed tomography (CT)</a>, <a href="https://publications.waset.org/abstracts/search?q=noise%20reduction" title=" noise reduction"> noise reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=curve-let" title=" curve-let"> curve-let</a>, <a href="https://publications.waset.org/abstracts/search?q=contour-let" title=" contour-let"> contour-let</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20to%20noise%20peak-peak%20ratio%20%28PSNR%29" title=" signal to noise peak-peak ratio (PSNR)"> signal to noise peak-peak ratio (PSNR)</a>, <a href="https://publications.waset.org/abstracts/search?q=structure%20similarity%20%28Ssim%29" title=" structure similarity (Ssim)"> structure similarity (Ssim)</a>, <a href="https://publications.waset.org/abstracts/search?q=absorbed%20dose%20to%20patient%20%28ADP%29" title=" absorbed dose to patient (ADP)"> absorbed dose to patient (ADP)</a> </p> <a href="https://publications.waset.org/abstracts/37368/ct-medical-images-denoising-based-on-new-wavelet-thresholding-compared-with-curvelet-and-contourlet" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37368.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">440</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2776</span> Anisotropic Total Fractional Order Variation Model in Seismic Data Denoising</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jianwei%20Ma">Jianwei Ma</a>, <a href="https://publications.waset.org/abstracts/search?q=Diriba%20Gemechu"> Diriba Gemechu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In seismic data processing, attenuation of random noise is the basic step to improve quality of data for further application of seismic data in exploration and development in different gas and oil industries. The signal-to-noise ratio of the data also highly determines quality of seismic data. This factor affects the reliability as well as the accuracy of seismic signal during interpretation for different purposes in different companies. To use seismic data for further application and interpretation, we need to improve the signal-to-noise ration while attenuating random noise effectively. To improve the signal-to-noise ration and attenuating seismic random noise by preserving important features and information about seismic signals, we introduce the concept of anisotropic total fractional order denoising algorithm. The anisotropic total fractional order variation model defined in fractional order bounded variation is proposed as a regularization in seismic denoising. The split Bregman algorithm is employed to solve the minimization problem of the anisotropic total fractional order variation model and the corresponding denoising algorithm for the proposed method is derived. We test the effectiveness of theproposed method for synthetic and real seismic data sets and the denoised result is compared with F-X deconvolution and non-local means denoising algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=anisotropic%20total%20fractional%20order%20variation" title="anisotropic total fractional order variation">anisotropic total fractional order variation</a>, <a href="https://publications.waset.org/abstracts/search?q=fractional%20order%20bounded%20variation" title=" fractional order bounded variation"> fractional order bounded variation</a>, <a href="https://publications.waset.org/abstracts/search?q=seismic%20random%20noise%20attenuation" title=" seismic random noise attenuation"> seismic random noise attenuation</a>, <a href="https://publications.waset.org/abstracts/search?q=split%20Bregman%20algorithm" title=" split Bregman algorithm"> split Bregman algorithm</a> </p> <a href="https://publications.waset.org/abstracts/77827/anisotropic-total-fractional-order-variation-model-in-seismic-data-denoising" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77827.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">207</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2775</span> Image Denoising Using Spatial Adaptive Mask Filter for Medical Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Sumalatha">R. Sumalatha</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20V.%20Subramanyam"> M. V. Subramanyam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In medical image processing the quality of the image is degraded in the presence of noise. Especially in ultra sound imaging and Magnetic resonance imaging the data was corrupted by signal dependent noise known as salt and pepper noise. Removal of noise from the medical images is a critical issue for researchers. In this paper, a new type of technique Adaptive Spatial Mask Filter (ASMF) has been proposed. The proposed filter is used to increase the quality of MRI and ultra sound images. Experimental results show that the proposed filter outperforms the implementation of mean, median, adaptive median filters in terms of MSE and PSNR. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=salt%20and%20pepper%20noise" title="salt and pepper noise">salt and pepper noise</a>, <a href="https://publications.waset.org/abstracts/search?q=ASMF" title=" ASMF"> ASMF</a>, <a href="https://publications.waset.org/abstracts/search?q=PSNR" title=" PSNR"> PSNR</a>, <a href="https://publications.waset.org/abstracts/search?q=MSE" title=" MSE"> MSE</a> </p> <a href="https://publications.waset.org/abstracts/3843/image-denoising-using-spatial-adaptive-mask-filter-for-medical-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3843.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">435</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2774</span> Denoising of Motor Unit Action Potential Based on Tunable Band-Pass Filter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khalida%20S.%20Rijab">Khalida S. Rijab</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20E.%20Safi"> Mohammed E. Safi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ayad%20A.%20%20Ibrahim"> Ayad A. Ibrahim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> When electrical electrodes are mounted on the skin surface of the muscle, a signal is detected when a skeletal muscle undergoes contraction; the signal is known as surface electromyographic signal (EMG). This signal has a noise-like interference pattern resulting from the temporal and spatial summation of action potentials (AP) of all active motor units (MU) near electrode detection. By appropriate processing (Decomposition), the surface EMG signal may be used to give an estimate of motor unit action potential. In this work, a denoising technique is applied to the MUAP signals extracted from the spatial filter (IB2). A set of signals from a non-invasive two-dimensional grid of 16 electrodes from different types of subjects, muscles, and sex are recorded. These signals will acquire noise during recording and detection. A digital fourth order band- pass Butterworth filter is used for denoising, with a tuned band-pass frequency of suitable choice of cutoff frequencies is investigated, with the aim of obtaining a suitable band pass frequency. Results show an improvement of (1-3 dB) in the signal to noise ratio (SNR) have been achieved, relative to the raw spatial filter output signals for all cases that were under investigation. Furthermore, the research鈥檚 goal included also estimation and reconstruction of the mean shape of the MUAP. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=EMG" title="EMG">EMG</a>, <a href="https://publications.waset.org/abstracts/search?q=Motor%20Unit" title=" Motor Unit"> Motor Unit</a>, <a href="https://publications.waset.org/abstracts/search?q=Digital%20Filter" title=" Digital Filter"> Digital Filter</a>, <a href="https://publications.waset.org/abstracts/search?q=Denoising" title=" Denoising"> Denoising</a> </p> <a href="https://publications.waset.org/abstracts/63012/denoising-of-motor-unit-action-potential-based-on-tunable-band-pass-filter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63012.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">401</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2773</span> Denoising Transient Electromagnetic Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lingerew%20Nebere%20Kassie">Lingerew Nebere Kassie</a>, <a href="https://publications.waset.org/abstracts/search?q=Ping-Yu%20Chang"> Ping-Yu Chang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hsin-Hua%20Huang"> Hsin-Hua Huang</a>, <a href="https://publications.waset.org/abstracts/search?q="></a>, <a href="https://publications.waset.org/abstracts/search?q=Chaw-Son%20Chen">Chaw-Son Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Transient electromagnetic (TEM) data plays a crucial role in hydrogeological and environmental applications, providing valuable insights into geological structures and resistivity variations. However, the presence of noise often hinders the interpretation and reliability of these data. Our study addresses this issue by utilizing a FASTSNAP system for the TEM survey, which operates at different modes (low, medium, and high) with continuous adjustments to discretization, gain, and current. We employ a denoising approach that processes the raw data obtained from each acquisition mode to improve signal quality and enhance data reliability. We use a signal-averaging technique for each mode, increasing the signal-to-noise ratio. Additionally, we utilize wavelet transform to suppress noise further while preserving the integrity of the underlying signals. This approach significantly improves the data quality, notably suppressing severe noise at late times. The resulting denoised data exhibits a substantially improved signal-to-noise ratio, leading to increased accuracy in parameter estimation. By effectively denoising TEM data, our study contributes to a more reliable interpretation and analysis of underground structures. Moreover, the proposed denoising approach can be seamlessly integrated into existing ground-based TEM data processing workflows, facilitating the extraction of meaningful information from noisy measurements and enhancing the overall quality and reliability of the acquired data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20quality" title="data quality">data quality</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20averaging" title=" signal averaging"> signal averaging</a>, <a href="https://publications.waset.org/abstracts/search?q=transient%20electromagnetic" title=" transient electromagnetic"> transient electromagnetic</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20transform" title=" wavelet transform"> wavelet transform</a> </p> <a href="https://publications.waset.org/abstracts/168409/denoising-transient-electromagnetic-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168409.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">84</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2772</span> Clothes Identification Using Inception ResNet V2 and MobileNet V2</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Subodh%20Chandra%20Shakya">Subodh Chandra Shakya</a>, <a href="https://publications.waset.org/abstracts/search?q=Badal%20Shrestha"> Badal Shrestha</a>, <a href="https://publications.waset.org/abstracts/search?q=Suni%20Thapa"> Suni Thapa</a>, <a href="https://publications.waset.org/abstracts/search?q=Ashutosh%20Chauhan"> Ashutosh Chauhan</a>, <a href="https://publications.waset.org/abstracts/search?q=Saugat%20Adhikari"> Saugat Adhikari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To tackle our problem of clothes identification, we used different architectures of Convolutional Neural Networks. Among different architectures, the outcome from Inception ResNet V2 and MobileNet V2 seemed promising. On comparison of the metrices, we observed that the Inception ResNet V2 slightly outperforms MobileNet V2 for this purpose. So this paper of ours proposes the cloth identifier using Inception ResNet V2 and also contains the comparison between the outcome of ResNet V2 and MobileNet V2. The document here contains the results and findings of the research that we performed on the DeepFashion Dataset. To improve the dataset, we used different image preprocessing techniques like image shearing, image rotation, and denoising. The whole experiment was conducted with the intention of testing the efficiency of convolutional neural networks on cloth identification so that we could develop a reliable system that is good enough in identifying the clothes worn by the users. The whole system can be integrated with some kind of recommendation system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=inception%20ResNet" title="inception ResNet">inception ResNet</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20net" title=" convolutional neural net"> convolutional neural net</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=confusion%20matrix" title=" confusion matrix"> confusion matrix</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20augmentation" title=" data augmentation"> data augmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20preprocessing" title=" data preprocessing"> data preprocessing</a> </p> <a href="https://publications.waset.org/abstracts/129604/clothes-identification-using-inception-resnet-v2-and-mobilenet-v2" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129604.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">187</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2771</span> Construction of the Large Scale Biological Networks from Microarrays</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fadhl%20Alakwaa">Fadhl Alakwaa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the sustainable goals of the system biology is understanding gene-gene interactions. Hence, gene regulatory networks (GRN) need to be constructed for understanding the disease ontology and to reduce the cost of drug development. To construct gene regulatory from gene expression we need to overcome many challenges such as data denoising and dimensionality. In this paper, we develop an integrated system to reduce data dimension and remove the noise. The generated network from our system was validated via available interaction databases and was compared to previous methods. The result revealed the performance of our proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gene%20regulatory%20network" title="gene regulatory network">gene regulatory network</a>, <a href="https://publications.waset.org/abstracts/search?q=biclustering" title=" biclustering"> biclustering</a>, <a href="https://publications.waset.org/abstracts/search?q=denoising" title=" denoising"> denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=system%20biology" title=" system biology"> system biology</a> </p> <a href="https://publications.waset.org/abstracts/74607/construction-of-the-large-scale-biological-networks-from-microarrays" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74607.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">239</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2770</span> QCARNet: Networks for Quality-Adaptive Compression Artifact</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seung%20Ho%20Park">Seung Ho Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Young%20Su%20Moon"> Young Su Moon</a>, <a href="https://publications.waset.org/abstracts/search?q=Nam%20Ik%20Cho"> Nam Ik Cho</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We propose a convolution neural network (CNN) for quality adaptive compression artifact reduction named QCARNet. The proposed method is different from the existing discriminative models that learn a specific model at a certain quality level. The method is composed of a quality estimation CNN (QECNN) and a compression artifact reduction CNN (CARCNN), which are two functionally separate CNNs. By connecting the QECNN and CARCNN, each CARCNN layer is able to adaptively reduce compression artifacts and preserve details depending on the estimated quality level map generated by the QECNN. We experimentally demonstrate that the proposed method achieves better performance compared to other state-of-the-art blind compression artifact reduction methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=compression%20artifact%20reduction" title="compression artifact reduction">compression artifact reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=deblocking" title=" deblocking"> deblocking</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20denoising" title=" image denoising"> image denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20restoration" title=" image restoration"> image restoration</a> </p> <a href="https://publications.waset.org/abstracts/108816/qcarnet-networks-for-quality-adaptive-compression-artifact" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/108816.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">139</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=image%20denoising&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=image%20denoising&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=image%20denoising&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=image%20denoising&page=5">5</a></li> <li class="page-item"><a class="page-link" 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