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image enhancement</title> <meta name="description" content="Search results for: fingerprint image enhancement"> <meta name="keywords" content="fingerprint image enhancement"> <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 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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="fingerprint image enhancement"> <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> 4161</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: fingerprint image enhancement</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4161</span> Scar Removal Stretegy for Fingerprint Using Diffusion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20A.%20U.%20Khan">Mohammad A. U. Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Tariq%20M.%20Khan"> Tariq M. Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Yinan%20Kong"> Yinan Kong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fingerprint image enhancement is one of the most important step in an automatic fingerprint identification recognition (AFIS) system which directly affects the overall efficiency of AFIS. The conventional fingerprint enhancement like Gabor and Anisotropic filters do fill the gaps in ridge lines but they fail to tackle scar lines. To deal with this problem we are proposing a method for enhancing the ridges and valleys with scar so that true minutia points can be extracted with accuracy. Our results have shown an improved performance in terms of enhancement. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fingerprint%20image%20enhancement" title="fingerprint image enhancement">fingerprint image enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=removing%20noise" title=" removing noise"> removing noise</a>, <a href="https://publications.waset.org/abstracts/search?q=coherence" title=" coherence"> coherence</a>, <a href="https://publications.waset.org/abstracts/search?q=enhanced%20diffusion" title=" enhanced diffusion"> enhanced diffusion</a> </p> <a href="https://publications.waset.org/abstracts/19427/scar-removal-stretegy-for-fingerprint-using-diffusion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19427.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">516</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">4160</span> Biimodal Biometrics System Using Fusion of Iris and Fingerprint</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Attallah%20Bilal">Attallah Bilal</a>, <a href="https://publications.waset.org/abstracts/search?q=Hendel%20Fatiha"> Hendel Fatiha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes the bimodal biometrics system for identity verification iris and fingerprint, at matching score level architecture using weighted sum of score technique. The features are extracted from the pre processed images of iris and fingerprint. These features of a query image are compared with those of a database image to obtain matching scores. The individual scores generated after matching are passed to the fusion module. This module consists of three major steps i.e., normalization, generation of similarity score and fusion of weighted scores. The final score is then used to declare the person as genuine or an impostor. The system is tested on CASIA database and gives an overall accuracy of 91.04% with FAR of 2.58% and FRR of 8.34%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=iris" title="iris">iris</a>, <a href="https://publications.waset.org/abstracts/search?q=fingerprint" title=" fingerprint"> fingerprint</a>, <a href="https://publications.waset.org/abstracts/search?q=sum%20rule" title=" sum rule"> sum rule</a>, <a href="https://publications.waset.org/abstracts/search?q=fusion" title=" fusion"> fusion</a> </p> <a href="https://publications.waset.org/abstracts/18556/biimodal-biometrics-system-using-fusion-of-iris-and-fingerprint" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18556.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">368</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">4159</span> The Linguistic Fingerprint in Western and Arab Judicial Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Asem%20Bani%20Amer">Asem Bani Amer</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study handles the linguistic fingerprint in judicial applications described in a law technicality that is recent and developing. It can be adopted to discover criminals by identifying their way of speaking and their special linguistic expressions. This is achieved by understanding the expression "linguistic fingerprint," its concept, and its extended domain, then revealing some of the linguistic fingerprint tools in Western judicial applications and deducing a technical imagination for a linguistic fingerprint in the Arabic language, which is needy for such judicial applications regarding this field, through dictionaries, language rhythm, and language structure. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=linguistic%20fingerprint" title="linguistic fingerprint">linguistic fingerprint</a>, <a href="https://publications.waset.org/abstracts/search?q=judicial" title=" judicial"> judicial</a>, <a href="https://publications.waset.org/abstracts/search?q=application" title=" application"> application</a>, <a href="https://publications.waset.org/abstracts/search?q=dictionary" title=" dictionary"> dictionary</a>, <a href="https://publications.waset.org/abstracts/search?q=picture" title=" picture"> picture</a>, <a href="https://publications.waset.org/abstracts/search?q=rhythm" title=" rhythm"> rhythm</a>, <a href="https://publications.waset.org/abstracts/search?q=structure" title=" structure"> structure</a> </p> <a href="https://publications.waset.org/abstracts/162132/the-linguistic-fingerprint-in-western-and-arab-judicial-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162132.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">81</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">4158</span> Multi-Spectral Medical Images Enhancement Using a Weber鈥檚 law</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muna%20F.%20Al-Sammaraie">Muna F. Al-Sammaraie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this research is to present a multi spectral image enhancement methods used to achieve highly real digital image populates only a small portion of the available range of digital values. Also, a quantitative measure of image enhancement is presented. This measure is related with concepts of the Webers Low of the human visual system. For decades, several image enhancement techniques have been proposed. Although most techniques require profuse amount of advance and critical steps, the result for the perceive image are not as satisfied. This study involves changing the original values so that more of the available range is used; then increases the contrast between features and their backgrounds. It consists of reading the binary image on the basis of pixels taking them byte-wise and displaying it, calculating the statistics of an image, automatically enhancing the color of the image based on statistics calculation using algorithms and working with RGB color bands. Finally, the enhanced image is displayed along with image histogram. A number of experimental results illustrated the performance of these algorithms. Particularly the quantitative measure has helped to select optimal processing parameters: the best parameters and transform. <p class="card-text"><strong>Keywords:</strong> <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=multi-spectral" title=" multi-spectral"> multi-spectral</a>, <a href="https://publications.waset.org/abstracts/search?q=RGB" title=" RGB"> RGB</a>, <a href="https://publications.waset.org/abstracts/search?q=histogram" title=" histogram"> histogram</a> </p> <a href="https://publications.waset.org/abstracts/8574/multi-spectral-medical-images-enhancement-using-a-webers-law" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8574.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">328</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">4157</span> UniFi: Universal Filter 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=Aleksei%20Samarin">Aleksei Samarin</a>, <a href="https://publications.waset.org/abstracts/search?q=Artyom%20Nazarenko"> Artyom Nazarenko</a>, <a href="https://publications.waset.org/abstracts/search?q=Valentin%20Malykh"> Valentin Malykh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image enhancement is becoming more and more popular, especially on mobile devices. Nowadays, it is a common approach to enhance an image using a convolutional neural network (CNN). Such a network should be of significant size; otherwise, a possibility for the artifacts to occur is overgrowing. The existing large CNNs are computationally expensive, which could be crucial for mobile devices. Another important flaw of such models is they are poorly interpretable. There is another approach to image enhancement, namely, the usage of predefined filters in combination with the prediction of their applicability. We present an approach following this paradigm, which outperforms both existing CNN-based and filter-based approaches in the image enhancement task. It is easily adaptable for mobile devices since it has only 47 thousand parameters. It shows the best SSIM 0.919 on RANDOM250 (MIT Adobe FiveK) among small models and is thrice faster than previous models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=universal%20filter" title="universal filter">universal filter</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=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a> </p> <a href="https://publications.waset.org/abstracts/151664/unifi-universal-filter-model-for-image-enhancement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151664.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">101</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">4156</span> Comparative Study of Different Enhancement Techniques for Computed Tomography Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=C.%20G.%20Jinimole">C. G. Jinimole</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Harsha"> A. Harsha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the key problems facing in the analysis of Computed Tomography (CT) images is the poor contrast of the images. Image enhancement can be used to improve the visual clarity and quality of the images or to provide a better transformation representation for further processing. Contrast enhancement of images is one of the acceptable methods used for image enhancement in various applications in the medical field. This will be helpful to visualize and extract details of brain infarctions, tumors, and cancers from the CT image. This paper presents a comparison study of five contrast enhancement techniques suitable for the contrast enhancement of CT images. The types of techniques include Power Law Transformation, Logarithmic Transformation, Histogram Equalization, Contrast Stretching, and Laplacian Transformation. All these techniques are compared with each other to find out which enhancement provides better contrast of CT image. For the comparison of the techniques, the parameters Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) are used. Logarithmic Transformation provided the clearer and best quality image compared to all other techniques studied and has got the highest value of PSNR. Comparison concludes with better approach for its future research especially for mapping abnormalities from CT images resulting from Brain Injuries. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computed%20tomography" title="computed tomography">computed tomography</a>, <a href="https://publications.waset.org/abstracts/search?q=enhancement%20techniques" title=" enhancement techniques"> enhancement techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=increasing%20contrast" title=" increasing contrast"> increasing contrast</a>, <a href="https://publications.waset.org/abstracts/search?q=PSNR%20and%20MSE" title=" PSNR and MSE"> PSNR and MSE</a> </p> <a href="https://publications.waset.org/abstracts/69868/comparative-study-of-different-enhancement-techniques-for-computed-tomography-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69868.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">314</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">4155</span> Design and Performance Analysis of Advanced B-Spline Algorithm for Image Resolution Enhancement</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Z.%20Kurian">M. Z. Kurian</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20V.%20Chidananda%20Murthy"> M. V. Chidananda Murthy</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20S.%20Guruprasad"> H. S. Guruprasad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An approach to super-resolve the low-resolution (LR) image is presented in this paper which is very useful in multimedia communication, medical image enhancement and satellite image enhancement to have a clear view of the information in the image. The proposed Advanced B-Spline method generates a high-resolution (HR) image from single LR image and tries to retain the higher frequency components such as edges in the image. This method uses B-Spline technique and Crispening. This work is evaluated qualitatively and quantitatively using Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). The method is also suitable for real-time applications. Different combinations of decimation and super-resolution algorithms in the presence of different noise and noise factors are tested. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=advanced%20b-spline" title="advanced b-spline">advanced b-spline</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20super-resolution" title=" image super-resolution"> image super-resolution</a>, <a href="https://publications.waset.org/abstracts/search?q=mean%20square%20error%20%28MSE%29" title=" mean square error (MSE)"> mean square error (MSE)</a>, <a href="https://publications.waset.org/abstracts/search?q=peak%20signal%20to%20noise%20ratio%20%28PSNR%29" title=" peak signal to noise ratio (PSNR)"> peak signal to noise ratio (PSNR)</a>, <a href="https://publications.waset.org/abstracts/search?q=resolution%20down%20converter" title=" resolution down converter"> resolution down converter</a> </p> <a href="https://publications.waset.org/abstracts/59499/design-and-performance-analysis-of-advanced-b-spline-algorithm-for-image-resolution-enhancement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59499.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">399</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">4154</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">509</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">4153</span> Fingerprint Image Encryption Using a 2D Chaotic Map and Elliptic Curve Cryptography</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=D.%20M.%20S.%20Bandara">D. M. S. Bandara</a>, <a href="https://publications.waset.org/abstracts/search?q=Yunqi%20Lei"> Yunqi Lei</a>, <a href="https://publications.waset.org/abstracts/search?q=Ye%20Luo"> Ye Luo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fingerprints are suitable as long-term markers of human identity since they provide detailed and unique individual features which are difficult to alter and durable over life time. In this paper, we propose an algorithm to encrypt and decrypt fingerprint images by using a specially designed Elliptic Curve Cryptography (ECC) procedure based on block ciphers. In addition, to increase the confusing effect of fingerprint encryption, we also utilize a chaotic-behaved method called Arnold Cat Map (ACM) for a 2D scrambling of pixel locations in our method. Experimental results are carried out with various types of efficiency and security analyses. As a result, we demonstrate that the proposed fingerprint encryption/decryption algorithm is advantageous in several different aspects including efficiency, security and flexibility. In particular, using this algorithm, we achieve a margin of about 0.1% in the test of Number of Pixel Changing Rate (NPCR) values comparing to the-state-of-the-art performances. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=arnold%20cat%20map" title="arnold cat map">arnold cat map</a>, <a href="https://publications.waset.org/abstracts/search?q=biometric%20encryption" title=" biometric encryption"> biometric encryption</a>, <a href="https://publications.waset.org/abstracts/search?q=block%20cipher" title=" block cipher"> block cipher</a>, <a href="https://publications.waset.org/abstracts/search?q=elliptic%20curve%20cryptography" title=" elliptic curve cryptography"> elliptic curve cryptography</a>, <a href="https://publications.waset.org/abstracts/search?q=fingerprint%20encryption" title=" fingerprint encryption"> fingerprint encryption</a>, <a href="https://publications.waset.org/abstracts/search?q=Koblitz%E2%80%99s%20%20encoding" title=" Koblitz鈥檚 encoding"> Koblitz鈥檚 encoding</a> </p> <a href="https://publications.waset.org/abstracts/96251/fingerprint-image-encryption-using-a-2d-chaotic-map-and-elliptic-curve-cryptography" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/96251.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">204</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">4152</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">4151</span> Image Enhancement of Histological Slides by Using Nonlinear Transfer Function</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=D.%20Suman">D. Suman</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Nikitha"> B. Nikitha</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Sarvani"> J. Sarvani</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Archana"> V. Archana</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Histological slides provide clinical diagnostic information about the subjects from the ancient times. Even with the advent of high resolution imaging cameras the image tend to have some background noise which makes the analysis complex. A study of the histological slides is done by using a nonlinear transfer function based image enhancement method. The method processes the raw, color images acquired from the biological microscope, which, in general, is associated with background noise. The images usually appearing blurred does not convey the intended information. In this regard, an enhancement method is proposed and implemented on 50 histological slides of human tissue by using nonlinear transfer function method. The histological image is converted into HSV color image. The luminance value of the image is enhanced (V component) because change in the H and S components could change the color balance between HSV components. The HSV image is divided into smaller blocks for carrying out the dynamic range compression by using a linear transformation function. Each pixel in the block is enhanced based on the contrast of the center pixel and its neighborhood. After the processing the V component, the HSV image is transformed into a colour image. The study has shown improvement of the characteristics of the image so that the significant details of the histological images were improved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=HSV%20space" title="HSV space">HSV space</a>, <a href="https://publications.waset.org/abstracts/search?q=histology" title=" histology"> histology</a>, <a href="https://publications.waset.org/abstracts/search?q=enhancement" title=" enhancement"> enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=image" title=" image"> image</a> </p> <a href="https://publications.waset.org/abstracts/12167/image-enhancement-of-histological-slides-by-using-nonlinear-transfer-function" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12167.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">329</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">4150</span> Pre-Processing of Ultrasonography Image Quality Improvement in Cases of Cervical Cancer Using Image Enhancement </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Retno%20Supriyanti">Retno Supriyanti</a>, <a href="https://publications.waset.org/abstracts/search?q=Teguh%20Budiono"> Teguh Budiono</a>, <a href="https://publications.waset.org/abstracts/search?q=Yogi%20Ramadhani"> Yogi Ramadhani</a>, <a href="https://publications.waset.org/abstracts/search?q=Haris%20B.%20Widodo"> Haris B. Widodo</a>, <a href="https://publications.waset.org/abstracts/search?q=Arwita%20Mulyawati"> Arwita Mulyawati</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cervical cancer is the leading cause of mortality in cancer-related diseases. In this diagnosis doctors usually perform several tests to determine the presence of cervical cancer in a patient. However, these checks require support equipment to get the results in more detail. One is by using ultrasonography. However, for the developing countries most of the existing ultrasonography has a low resolution. The goal of this research is to obtain abnormalities on low-resolution ultrasound images especially for cervical cancer case. In this paper, we emphasize our work to use Image Enhancement for pre-processing image quality improvement. The result shows that pre-processing stage is promising to support further analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cervical%20cancer" title="cervical cancer">cervical cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=mortality" title=" mortality"> mortality</a>, <a href="https://publications.waset.org/abstracts/search?q=low-resolution" title=" low-resolution"> low-resolution</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20enhancement." title=" image enhancement. "> image enhancement. </a> </p> <a href="https://publications.waset.org/abstracts/26523/pre-processing-of-ultrasonography-image-quality-improvement-in-cases-of-cervical-cancer-using-image-enhancement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26523.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">636</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">4149</span> Color Image Enhancement Using Multiscale Retinex and Image Fusion Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chang-Hsing%20Lee">Chang-Hsing Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Cheng-Chang%20Lien"> Cheng-Chang Lien</a>, <a href="https://publications.waset.org/abstracts/search?q=Chin-Chuan%20Han"> Chin-Chuan Han</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, an edge-strength guided multiscale retinex (EGMSR) approach will be proposed for color image contrast enhancement. In EGMSR, the pixel-dependent weight associated with each pixel in the single scale retinex output image is computed according to the edge strength around this pixel in order to prevent from over-enhancing the noises contained in the smooth dark/bright regions. Further, by fusing together the enhanced results of EGMSR and adaptive multiscale retinex (AMSR), we can get a natural fused image having high contrast and proper tonal rendition. Experimental results on several low-contrast images have shown that our proposed approach can produce natural and appealing enhanced images. <p class="card-text"><strong>Keywords:</strong> <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=multiscale%20retinex" title=" multiscale retinex"> multiscale retinex</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20fusion" title=" image fusion"> image fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=EGMSR" title=" EGMSR"> EGMSR</a> </p> <a href="https://publications.waset.org/abstracts/15139/color-image-enhancement-using-multiscale-retinex-and-image-fusion-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15139.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">458</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">4148</span> The Use of Image Processing Responses Tools Applied to Analysing Bouguer Gravity Anomaly Map (Tangier-Tetuan's Area-Morocco)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saad%20Bakkali">Saad Bakkali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image processing is a powerful tool for the enhancement of edges in images used in the interpretation of geophysical potential field data. Arial and terrestrial gravimetric surveys were carried out in the region of Tangier-Tetuan. From the observed and measured data of gravity Bouguer gravity anomalies map was prepared. This paper reports the results and interpretations of the transformed maps of Bouguer gravity anomaly of the Tangier-Tetuan area using image processing. Filtering analysis based on classical image process was applied. Operator image process like logarithmic and gamma correction are used. This paper also present the results obtained from this image processing analysis of the enhancement edges of the Bouguer gravity anomaly map of the Tangier-Tetuan zone. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bouguer" title="bouguer">bouguer</a>, <a href="https://publications.waset.org/abstracts/search?q=tangier" title=" tangier"> tangier</a>, <a href="https://publications.waset.org/abstracts/search?q=filtering" title=" filtering"> filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=gamma%20correction" title=" gamma correction"> gamma correction</a>, <a href="https://publications.waset.org/abstracts/search?q=logarithmic%20enhancement%20edges" title=" logarithmic enhancement edges"> logarithmic enhancement edges</a> </p> <a href="https://publications.waset.org/abstracts/36524/the-use-of-image-processing-responses-tools-applied-to-analysing-bouguer-gravity-anomaly-map-tangier-tetuans-area-morocco" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36524.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">422</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">4147</span> Vector Quantization Based on Vector Difference Scheme for Image Enhancement</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Biji%20Jacob">Biji Jacob</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Vector quantization algorithm which uses minimum distance calculation for codebook generation, a time consuming calculation performed on each pixel values leads to computation complexity. The codebook is updated by comparing the distance of each vector to their centroid vector and measure for their closeness. In this paper vector quantization is modified based on vector difference algorithm for image enhancement purpose. In the proposed scheme, vector differences between the vectors are considered as the new generation vectors or new codebook vectors. The codebook is updated by comparing the new generation vector with a threshold value having minimum error with the parent vector. The minimum error decides the fitness of each newly generated vector. Thus the codebook is generated in an adaptive manner and the fitness value is determined for the suppression of the degraded portion of the image and thereby leads to the enhancement of the image through the adaptive searching capability of the vector quantization through vector difference algorithm. Experimental results shows that the vector difference scheme efficiently modifies the vector quantization algorithm for enhancing the image with peak signal to noise ratio (PSNR), mean square error (MSE), Euclidean distance (E_dist) as the performance parameters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=codebook" title="codebook">codebook</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=vector%20difference" title=" vector difference"> vector difference</a>, <a href="https://publications.waset.org/abstracts/search?q=vector%20quantization" title=" vector quantization"> vector quantization</a> </p> <a href="https://publications.waset.org/abstracts/39597/vector-quantization-based-on-vector-difference-scheme-for-image-enhancement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39597.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">267</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">4146</span> Detection of Image Blur and Its Restoration for Image Enhancement</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20V.%20Chidananda%20Murthy">M. V. Chidananda Murthy</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Z.%20Kurian"> M. Z. Kurian</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20S.%20Guruprasad"> H. S. Guruprasad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image restoration in the process of communication is one of the emerging fields in the image processing. The motion analysis processing is the simplest case to detect motion in an image. Applications of motion analysis widely spread in many areas such as surveillance, remote sensing, film industry, navigation of autonomous vehicles, etc. The scene may contain multiple moving objects, by using motion analysis techniques the blur caused by the movement of the objects can be enhanced by filling-in occluded regions and reconstruction of transparent objects, and it also removes the motion blurring. This paper presents the design and comparison of various motion detection and enhancement filters. Median filter, Linear image deconvolution, Inverse filter, Pseudoinverse filter, Wiener filter, Lucy Richardson filter and Blind deconvolution filters are used to remove the blur. In this work, we have considered different types and different amount of blur for the analysis. Mean Square Error (MSE) and Peak Signal to Noise Ration (PSNR) are used to evaluate the performance of the filters. The designed system has been implemented in Matlab software and tested for synthetic and real-time images. <p class="card-text"><strong>Keywords:</strong> <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=motion%20analysis" title=" motion analysis"> motion analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=motion%20detection" title=" motion detection"> motion detection</a>, <a href="https://publications.waset.org/abstracts/search?q=motion%20estimation" title=" motion estimation"> motion estimation</a> </p> <a href="https://publications.waset.org/abstracts/59485/detection-of-image-blur-and-its-restoration-for-image-enhancement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59485.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">287</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">4145</span> Adaptive Dehazing Using Fusion Strategy </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Ramesh%20Kanthan">M. Ramesh Kanthan</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Naga%20Nandini%20Sujatha"> S. Naga Nandini Sujatha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The goal of haze removal algorithms is to enhance and recover details of scene from foggy image. In enhancement the proposed method focus into two main categories: (i) image enhancement based on Adaptive contrast Histogram equalization, and (ii) image edge strengthened Gradient model. Many circumstances accurate haze removal algorithms are needed. The de-fog feature works through a complex algorithm which first determines the fog destiny of the scene, then analyses the obscured image before applying contrast and sharpness adjustments to the video in real-time to produce image the fusion strategy is driven by the intrinsic properties of the original image and is highly dependent on the choice of the inputs and the weights. Then the output haze free image has reconstructed using fusion methodology. In order to increase the accuracy, interpolation method has used in the output reconstruction. A promising retrieval performance is achieved especially in particular examples. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=single%20image" title="single image">single image</a>, <a href="https://publications.waset.org/abstracts/search?q=fusion" title=" fusion"> fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=dehazing" title=" dehazing"> dehazing</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-scale%20fusion" title=" multi-scale fusion"> multi-scale fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=per-pixel" title=" per-pixel"> per-pixel</a>, <a href="https://publications.waset.org/abstracts/search?q=weight%20map" title=" weight map"> weight map</a> </p> <a href="https://publications.waset.org/abstracts/32544/adaptive-dehazing-using-fusion-strategy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32544.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">464</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">4144</span> Enhancement of X-Rays Images Intensity Using Pixel Values Adjustments Technique</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=Razan%20Manofely"> Razan Manofely</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajab%20M.%20Ben%20Yousef"> Rajab M. Ben Yousef</a> </p> <p class="card-text"><strong>Abstract:</strong></p> X-Ray images are very popular as a first tool for diagnosis. Automating the process of analysis of such images is important in order to help physician procedures. In this practice, teeth segmentation from the radiographic images and feature extraction are essential steps. The main objective of this study was to study correction preprocessing of x-rays images using local adaptive filters in order to evaluate contrast enhancement pattern in different x-rays images such as grey color and to evaluate the usage of new nonlinear approach for contrast enhancement of soft tissues in x-rays images. The data analyzed by using MatLab program to enhance the contrast within the soft tissues, the gray levels in both enhanced and unenhanced images and noise variance. The main techniques of enhancement used in this study were contrast enhancement filtering and deblurring images using the blind deconvolution algorithm. In this paper, prominent constraints are firstly preservation of image's overall look; secondly, preservation of the diagnostic content in the image and thirdly detection of small low contrast details in diagnostic content of the image. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=enhancement" title="enhancement">enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=x-rays" title=" x-rays"> x-rays</a>, <a href="https://publications.waset.org/abstracts/search?q=pixel%20intensity%20values" title=" pixel intensity values"> pixel intensity values</a>, <a href="https://publications.waset.org/abstracts/search?q=MatLab" title=" MatLab"> MatLab</a> </p> <a href="https://publications.waset.org/abstracts/31031/enhancement-of-x-rays-images-intensity-using-pixel-values-adjustments-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31031.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">485</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">4143</span> Contrast Enhancement of Masses in Mammograms Using Multiscale Morphology</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amit%20Kamra">Amit Kamra</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20K.%20Jain"> V. K. Jain</a>, <a href="https://publications.waset.org/abstracts/search?q=Pragya"> Pragya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mammography is widely used technique for breast cancer screening. There are various other techniques for breast cancer screening but mammography is the most reliable and effective technique. The images obtained through mammography are of low contrast which causes problem for the radiologists to interpret. Hence, a high quality image is mandatory for the processing of the image for extracting any kind of information from it. Many contrast enhancement algorithms have been developed over the years. In the present work, an efficient morphology based technique is proposed for contrast enhancement of masses in mammographic images. The proposed method is based on Multiscale Morphology and it takes into consideration the scale of the structuring element. The proposed method is compared with other state-of-the-art techniques. The experimental results show that the proposed method is better both qualitatively and quantitatively than the other standard contrast enhancement techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=enhancement" title="enhancement">enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=mammography" title=" mammography"> mammography</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-scale" title=" multi-scale"> multi-scale</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematical%20morphology" title=" mathematical morphology"> mathematical morphology</a> </p> <a href="https://publications.waset.org/abstracts/29677/contrast-enhancement-of-masses-in-mammograms-using-multiscale-morphology" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29677.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">427</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">4142</span> Integrated Intensity and Spatial Enhancement Technique for Color Images </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Evan%20W.%20Krieger">Evan W. Krieger</a>, <a href="https://publications.waset.org/abstracts/search?q=Vijayan%20K.%20Asari"> Vijayan K. Asari</a>, <a href="https://publications.waset.org/abstracts/search?q=Saibabu%20Arigela"> Saibabu Arigela</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Video imagery captured for real-time security and surveillance applications is typically captured in complex lighting conditions. These less than ideal conditions can result in imagery that can have underexposed or overexposed regions. It is also typical that the video is too low in resolution for certain applications. The purpose of security and surveillance video is that we should be able to make accurate conclusions based on the images seen in the video. Therefore, if poor lighting and low resolution conditions occur in the captured video, the ability to make accurate conclusions based on the received information will be reduced. We propose a solution to this problem by using image preprocessing to improve these images before use in a particular application. The proposed algorithm will integrate an intensity enhancement algorithm with a super resolution technique. The intensity enhancement portion consists of a nonlinear inverse sign transformation and an adaptive contrast enhancement. The super resolution section is a single image super resolution technique is a Fourier phase feature based method that uses a machine learning approach with kernel regression. The proposed technique intelligently integrates these algorithms to be able to produce a high quality output while also being more efficient than the sequential use of these algorithms. This integration is accomplished by performing the proposed algorithm on the intensity image produced from the original color image. After enhancement and super resolution, a color restoration technique is employed to obtain an improved visibility color image. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dynamic%20range%20compression" title="dynamic range compression">dynamic range compression</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-level%20Fourier%20features" title=" multi-level Fourier features"> multi-level Fourier features</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20enhancement" title=" nonlinear enhancement"> nonlinear enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=super%20resolution" title=" super resolution"> super resolution</a> </p> <a href="https://publications.waset.org/abstracts/22706/integrated-intensity-and-spatial-enhancement-technique-for-color-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22706.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">554</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">4141</span> Contrast Enhancement in Digital Images Using an Adaptive Unsharp Masking Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Z.%20Mortezaie">Z. Mortezaie</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Hassanpour"> H. Hassanpour</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Asadi%20Amiri"> S. Asadi Amiri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Captured images may suffer from Gaussian blur due to poor lens focus or camera motion. Unsharp masking is a simple and effective technique to boost the image contrast and to improve digital images suffering from Gaussian blur. The technique is based on sharpening object edges by appending the scaled high-frequency components of the image to the original. The quality of the enhanced image is highly dependent on the characteristics of both the high-frequency components and the scaling/gain factor. Since the quality of an image may not be the same throughout, we propose an adaptive unsharp masking method in this paper. In this method, the gain factor is computed, considering the gradient variations, for individual pixels of the image. Subjective and objective image quality assessments are used to compare the performance of the proposed method both with the classic and the recently developed unsharp masking methods. The experimental results show that the proposed method has a better performance in comparison to the other existing methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=unsharp%20masking" title="unsharp masking">unsharp masking</a>, <a href="https://publications.waset.org/abstracts/search?q=blur%20image" title=" blur image"> blur image</a>, <a href="https://publications.waset.org/abstracts/search?q=sub-region%20gradient" title=" sub-region gradient"> sub-region gradient</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20enhancement" title=" image enhancement"> image enhancement</a> </p> <a href="https://publications.waset.org/abstracts/73795/contrast-enhancement-in-digital-images-using-an-adaptive-unsharp-masking-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73795.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">214</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">4140</span> Development of Zinc Oxide Coated Carbon Nanoparticles from Pineapples Leaves Using SOL Gel Method for Optimal Adsorption of Copper ion and Reuse in Latent Fingerprint</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bienvenu%20Gael%20Fouda%20Mbanga">Bienvenu Gael Fouda Mbanga</a>, <a href="https://publications.waset.org/abstracts/search?q=Zikhona%20Tywabi-Ngeva"> Zikhona Tywabi-Ngeva</a>, <a href="https://publications.waset.org/abstracts/search?q=Kriveshini%20Pillay"> Kriveshini Pillay</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work highlighted a new method for preparing Nitrogen carbon nanoparticles fused on zinc oxide nanoparticle nanocomposite (N-CNPs/ZnONPsNC) to remove copper ions (Cu虏+) from wastewater by sol-gel method and applying the metal-loaded adsorbent in latent fingerprint application. The N-CNPs/ZnONPsNC showed to be an effective sorbent for optimum Cu虏+ sorption at pH 8 and 0.05 g dose. The Langmuir isotherm was found to best fit the process, with a maximum adsorption capacity of 285.71 mg/g, which was higher than most values found in other research for Cu虏+ removal. Adsorption was spontaneous and endothermic at 25oC. In addition, the Cu虏+-N-CNPs/ZnONPsNC was found to be sensitive and selective for latent fingerprint (LFP) recognition on a range of porous surfaces. As a result, in forensic research, it is an effective distinguishing chemical for latent fingerprint detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=latent%20fingerprint" title="latent fingerprint">latent fingerprint</a>, <a href="https://publications.waset.org/abstracts/search?q=nanocomposite" title=" nanocomposite"> nanocomposite</a>, <a href="https://publications.waset.org/abstracts/search?q=adsorption" title=" adsorption"> adsorption</a>, <a href="https://publications.waset.org/abstracts/search?q=copper%20ions" title=" copper ions"> copper ions</a>, <a href="https://publications.waset.org/abstracts/search?q=metal%20loaded%20adsorption" title=" metal loaded adsorption"> metal loaded adsorption</a>, <a href="https://publications.waset.org/abstracts/search?q=adsorbent" title=" adsorbent"> adsorbent</a> </p> <a href="https://publications.waset.org/abstracts/166109/development-of-zinc-oxide-coated-carbon-nanoparticles-from-pineapples-leaves-using-sol-gel-method-for-optimal-adsorption-of-copper-ion-and-reuse-in-latent-fingerprint" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/166109.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">4139</span> New Variational Approach for Contrast Enhancement of Color Image</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wanhyun%20Cho">Wanhyun Cho</a>, <a href="https://publications.waset.org/abstracts/search?q=Seongchae%20Seo"> Seongchae Seo</a>, <a href="https://publications.waset.org/abstracts/search?q=Soonja%20Kang"> Soonja Kang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, we propose a variational technique for image contrast enhancement which utilizes global and local information around each pixel. The energy functional is defined by a weighted linear combination of three terms which are called on a local, a global contrast term and dispersion term. The first one is a local contrast term that can lead to improve the contrast of an input image by increasing the grey-level differences between each pixel and its neighboring to utilize contextual information around each pixel. The second one is global contrast term, which can lead to enhance a contrast of image by minimizing the difference between its empirical distribution function and a cumulative distribution function to make the probability distribution of pixel values becoming a symmetric distribution about median. The third one is a dispersion term that controls the departure between new pixel value and pixel value of original image while preserving original image characteristics as well as possible. Second, we derive the Euler-Lagrange equation for true image that can achieve the minimum of a proposed functional by using the fundamental lemma for the calculus of variations. And, we considered the procedure that this equation can be solved by using a gradient decent method, which is one of the dynamic approximation techniques. Finally, by conducting various experiments, we can demonstrate that the proposed method can enhance the contrast of colour images better than existing techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=color%20image" title="color image">color image</a>, <a href="https://publications.waset.org/abstracts/search?q=contrast%20enhancement%20technique" title=" contrast enhancement technique"> contrast enhancement technique</a>, <a href="https://publications.waset.org/abstracts/search?q=variational%20approach" title=" variational approach"> variational approach</a>, <a href="https://publications.waset.org/abstracts/search?q=Euler-Lagrang%20equation" title=" Euler-Lagrang equation"> Euler-Lagrang equation</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20approximation%20method" title=" dynamic approximation method"> dynamic approximation method</a>, <a href="https://publications.waset.org/abstracts/search?q=EME%20measure" title=" EME measure"> EME measure</a> </p> <a href="https://publications.waset.org/abstracts/10574/new-variational-approach-for-contrast-enhancement-of-color-image" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10574.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">450</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">4138</span> A Review on Artificial Neural Networks in Image Processing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Afsharipoor">B. Afsharipoor</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Nazemi"> E. Nazemi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Artificial neural networks (ANNs) are powerful tool for prediction which can be trained based on a set of examples and thus, it would be useful for nonlinear image processing. The present paper reviews several paper regarding applications of ANN in image processing to shed the light on advantage and disadvantage of ANNs in this field. Different steps in the image processing chain including pre-processing, enhancement, segmentation, object recognition, image understanding and optimization by using ANN are summarized. Furthermore, results on using multi artificial neural networks are presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title="neural networks">neural networks</a>, <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=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20recognition" title=" object recognition"> object recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20understanding" title=" image understanding"> image understanding</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=MANN" title=" MANN"> MANN</a> </p> <a href="https://publications.waset.org/abstracts/36843/a-review-on-artificial-neural-networks-in-image-processing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36843.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">407</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">4137</span> Underwater Image Enhancement and Reconstruction Using CNN and the MultiUNet Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Snehal%20G.%20Teli">Snehal G. Teli</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20J.%20Shelke"> R. J. Shelke</a> </p> <p class="card-text"><strong>Abstract:</strong></p> CNN and MultiUNet models are the framework for the proposed method for enhancing and reconstructing underwater images. Multiscale merging of features and regeneration are both performed by the MultiUNet. CNN collects relevant features. Extensive tests on benchmark datasets show that the proposed strategy performs better than the latest methods. As a result of this work, underwater images can be represented and interpreted in a number of underwater applications with greater clarity. This strategy will advance underwater exploration and marine research by enhancing real-time underwater image processing systems, underwater robotic vision, and underwater surveillance. <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=image%20enhancement" title=" image enhancement"> image enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=multiunet" title=" multiunet"> multiunet</a>, <a href="https://publications.waset.org/abstracts/search?q=underwater%20images" title=" underwater images"> underwater images</a> </p> <a href="https://publications.waset.org/abstracts/170260/underwater-image-enhancement-and-reconstruction-using-cnn-and-the-multiunet-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170260.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">75</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">4136</span> HR MRI CS Based Image Reconstruction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Krzysztof%20Malczewski">Krzysztof Malczewski</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Magnetic Resonance Imaging (MRI) reconstruction algorithm using compressed sensing is presented in this paper. It is exhibited that the offered approach improves MR images spatial resolution in circumstances when highly undersampled k-space trajectories are applied. Compressed Sensing (CS) aims at signal and images reconstructing from significantly fewer measurements than were conventionally assumed necessary. Magnetic Resonance Imaging (MRI) is a fundamental medical imaging method struggles with an inherently slow data acquisition process. The use of CS to MRI has the potential for significant scan time reductions, with visible benefits for patients and health care economics. In this study the objective is to combine super-resolution image enhancement algorithm with CS framework benefits to achieve high resolution MR output image. Both methods emphasize on maximizing image sparsity on known sparse transform domain and minimizing fidelity. The presented algorithm considers the cardiac and respiratory movements. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=super-resolution" title="super-resolution">super-resolution</a>, <a href="https://publications.waset.org/abstracts/search?q=MRI" title=" MRI"> MRI</a>, <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=sparse-sense" title=" sparse-sense"> sparse-sense</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20enhancement" title=" image enhancement"> image enhancement</a> </p> <a href="https://publications.waset.org/abstracts/6021/hr-mri-cs-based-image-reconstruction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6021.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">430</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">4135</span> Medical Images Enhancement Using New Dynamic Band Pass Filter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdellatif%20Baba">Abdellatif Baba</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to facilitate medical images analysis by improving their quality and readability, we present in this paper a new dynamic band pass filter as a general and suitable operator for different types of medical images. Our objective is to enrich the details of any treated medical image to make it sufficiently clear enough to give an understood and simplified meaning even for unspecialized people in the medical domain. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=medical%20image%20enhancement" title="medical image enhancement">medical image enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20band%20pass%20filter" title=" dynamic band pass filter"> dynamic band pass filter</a>, <a href="https://publications.waset.org/abstracts/search?q=analysis%20improvement" title=" analysis improvement"> analysis improvement</a> </p> <a href="https://publications.waset.org/abstracts/14660/medical-images-enhancement-using-new-dynamic-band-pass-filter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14660.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">290</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">4134</span> FLIME - Fast Low Light Image Enhancement for Real-Time Video</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vinay%20P.">Vinay P.</a>, <a href="https://publications.waset.org/abstracts/search?q=Srinivas%20K.%20S."> Srinivas K. S.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Low Light Image Enhancement is of utmost impor- tance in computer vision based tasks. Applications include vision systems for autonomous driving, night vision devices for defence systems, low light object detection tasks. Many of the existing deep learning methods are resource intensive during the inference step and take considerable time for processing. The algorithm should take considerably less than 41 milliseconds in order to process a real-time video feed with 24 frames per second and should be even less for a video with 30 or 60 frames per second. The paper presents a fast and efficient solution which has two main advantages, it has the potential to be used for a real-time video feed, and it can be used in low compute environments because of the lightweight nature. The proposed solution is a pipeline of three steps, the first one is the use of a simple function to map input RGB values to output RGB values, the second is to balance the colors and the final step is to adjust the contrast of the image. Hence a custom dataset is carefully prepared using images taken in low and bright lighting conditions. The preparation of the dataset, the proposed model, the processing time are discussed in detail and the quality of the enhanced images using different methods is shown. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=low%20light%20image%20enhancement" title="low light image enhancement">low light image enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time%20video" title=" real-time video"> real-time video</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/144526/flime-fast-low-light-image-enhancement-for-real-time-video" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144526.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">206</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">4133</span> Biometric Identification with Latitude and Longitude Fingerprint Verification for Attendance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Fezan%20Afzal">Muhammad Fezan Afzal</a>, <a href="https://publications.waset.org/abstracts/search?q=Imran%20Khan"> Imran Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Salma%20Imtiaz"> Salma Imtiaz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The need for human verification and identification requires from centuries for authentication. Since it is being used in big institutes like financial, government and crime departments, a continued struggle is important to make this system more efficient to prevent security breaches. Therefore, multiple devices are used to authenticate the biometric for each individual. A large number of devices are required to cover a large number of users. As the number of devices increases, cost will automatically increase. Furthermore, it is time-consuming for biometrics due to the devices being insufficient and are not available at every door. In this paper, we propose the framework and algorithm where the mobile of each individual can also perform the biometric authentication of attendance and security. Every mobile has a biometric authentication system that is used in different mobile applications for security purposes. Therefore, each individual can use the biometric system mobile without moving from one place to another. Moreover, by using the biometrics mobile, the cost of biometric systems can be removed that are mostly deployed in different organizations for the attendance of students, employees and for other security purposes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fingerprint" title="fingerprint">fingerprint</a>, <a href="https://publications.waset.org/abstracts/search?q=fingerprint%20authentication" title=" fingerprint authentication"> fingerprint authentication</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20verification" title=" mobile verification"> mobile verification</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20biometric%20verification" title=" mobile biometric verification"> mobile biometric verification</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20fingerprint%20sensor" title=" mobile fingerprint sensor"> mobile fingerprint sensor</a> </p> <a href="https://publications.waset.org/abstracts/171752/biometric-identification-with-latitude-and-longitude-fingerprint-verification-for-attendance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171752.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">69</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">4132</span> 3D Guided Image Filtering to Improve Quality of Short-Time Binned Dynamic PET Images Using MRI Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tabassum%20Husain">Tabassum Husain</a>, <a href="https://publications.waset.org/abstracts/search?q=Shen%20Peng%20Li"> Shen Peng Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhaolin%20Chen"> Zhaolin Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper evaluates the usability of 3D Guided Image Filtering to enhance the quality of short-time binned dynamic PET images by using MRI images. Guided image filtering is an edge-preserving filter proposed to enhance 2D images. The 3D filter is applied on 1 and 5-minute binned images. The results are compared with 15-minute binned images and the Gaussian filtering. The guided image filter enhances the quality of dynamic PET images while also preserving important information of the voxels. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dynamic%20PET%20images" title="dynamic PET images">dynamic PET images</a>, <a href="https://publications.waset.org/abstracts/search?q=guided%20image%20filter" title=" guided image filter"> guided image filter</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=information%20preservation%20filtering" title=" information preservation filtering"> information preservation filtering</a> </p> <a href="https://publications.waset.org/abstracts/152864/3d-guided-image-filtering-to-improve-quality-of-short-time-binned-dynamic-pet-images-using-mri-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152864.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">132</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=fingerprint%20image%20enhancement&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=fingerprint%20image%20enhancement&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=fingerprint%20image%20enhancement&page=4">4</a></li> <li class="page-item"><a class="page-link" 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