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[2411.10798] Unveiling Hidden Details: A RAW Data-Enhanced Paradigm for Real-World Super-Resolution
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Existing Real SR methods primarily focus on generating details from the LR RGB domain, often leading to a lack of richness or fidelity in fine details. In this paper, we pioneer the use of details hidden in RAW data to complement existing RGB-only methods, yielding superior outputs. We argue that key image processing steps in Image Signal Processing, such as denoising and demosaicing, inherently result in the loss of fine details in LR images, making LR RAW a valuable information source. To validate this, we present RealSR-RAW, a comprehensive dataset comprising over 10,000 pairs with LR and HR RGB images, along with corresponding LR RAW, captured across multiple smartphones under varying focal lengths and diverse scenes. Additionally, we propose a novel, general RAW adapter to efficiently integrate LR RAW data into existing CNNs, Transformers, and Diffusion-based Real SR models by suppressing the noise contained in LR RAW and aligning its distribution. Extensive experiments demonstrate that incorporating RAW data significantly enhances detail recovery and improves Real SR performance across ten evaluation metrics, including both fidelity and perception-oriented metrics. Our findings open a new direction for the Real SR task, with the dataset and code will be made available to support future research."/> <meta name="twitter:site" content="@arxiv"/> <meta name="twitter:card" content="summary"/> <meta name="twitter:title" content="Unveiling Hidden Details: A RAW Data-Enhanced Paradigm for..."/> <meta name="twitter:description" content="Real-world image super-resolution (Real SR) aims to generate high-fidelity, detail-rich high-resolution (HR) images from low-resolution (LR) counterparts. Existing Real SR methods primarily focus..."/> <meta name="twitter:image" content="https://static.arxiv.org/icons/twitter/arxiv-logo-twitter-square.png"/> <meta name="twitter:image:alt" content="arXiv logo"/> <link rel="stylesheet" media="screen" type="text/css" href="/static/browse/0.3.4/css/tooltip.css"/><link rel="stylesheet" media="screen" type="text/css" href="https://static.arxiv.org/js/bibex-dev/bibex.css?20200709"/> <script src="/static/browse/0.3.4/js/mathjaxToggle.min.js" type="text/javascript"></script> <script src="//code.jquery.com/jquery-latest.min.js" type="text/javascript"></script> <script src="//cdn.jsdelivr.net/npm/js-cookie@2/src/js.cookie.min.js" type="text/javascript"></script> <script src="//cdn.jsdelivr.net/npm/dompurify@2.3.5/dist/purify.min.js"></script> <script src="/static/browse/0.3.4/js/toggle-labs.js?20241022" type="text/javascript"></script> <script src="/static/browse/0.3.4/js/cite.js" type="text/javascript"></script><meta name="citation_title" content="Unveiling Hidden Details: A RAW Data-Enhanced Paradigm for Real-World Super-Resolution" /><meta name="citation_author" content="Peng, Long" /><meta name="citation_author" content="Li, Wenbo" /><meta name="citation_author" content="Guo, Jiaming" /><meta name="citation_author" content="Di, Xin" /><meta name="citation_author" content="Sun, Haoze" /><meta name="citation_author" content="Li, Yong" /><meta name="citation_author" content="Pei, Renjing" /><meta name="citation_author" content="Wang, Yang" /><meta name="citation_author" content="Cao, Yang" /><meta name="citation_author" content="Zha, Zheng-Jun" /><meta name="citation_date" content="2024/11/16" /><meta name="citation_online_date" content="2024/11/21" /><meta name="citation_pdf_url" content="http://arxiv.org/pdf/2411.10798" /><meta name="citation_arxiv_id" content="2411.10798" /><meta name="citation_abstract" content="Real-world image super-resolution (Real SR) aims to generate high-fidelity, detail-rich high-resolution (HR) images from low-resolution (LR) counterparts. Existing Real SR methods primarily focus on generating details from the LR RGB domain, often leading to a lack of richness or fidelity in fine details. In this paper, we pioneer the use of details hidden in RAW data to complement existing RGB-only methods, yielding superior outputs. We argue that key image processing steps in Image Signal Processing, such as denoising and demosaicing, inherently result in the loss of fine details in LR images, making LR RAW a valuable information source. To validate this, we present RealSR-RAW, a comprehensive dataset comprising over 10,000 pairs with LR and HR RGB images, along with corresponding LR RAW, captured across multiple smartphones under varying focal lengths and diverse scenes. Additionally, we propose a novel, general RAW adapter to efficiently integrate LR RAW data into existing CNNs, Transformers, and Diffusion-based Real SR models by suppressing the noise contained in LR RAW and aligning its distribution. Extensive experiments demonstrate that incorporating RAW data significantly enhances detail recovery and improves Real SR performance across ten evaluation metrics, including both fidelity and perception-oriented metrics. Our findings open a new direction for the Real SR task, with the dataset and code will be made available to support future research." /> </head> <body class="with-cu-identity"> <div class="flex-wrap-footer"> <header> <a href="#content" class="is-sr-only">Skip to main content</a> <!-- start desktop header --> <div class="columns is-vcentered is-hidden-mobile" id="cu-identity"> <div class="column" id="cu-logo"> <a href="https://www.cornell.edu/"><img src="/static/browse/0.3.4/images/icons/cu/cornell-reduced-white-SMALL.svg" alt="Cornell University" /></a> </div><div class="column" id="support-ack"> <span id="support-ack-url">We gratefully acknowledge support from the Simons Foundation, <a href="https://info.arxiv.org/about/ourmembers.html">member institutions</a>, and all contributors.</span> <a href="https://info.arxiv.org/about/donate.html" class="btn-header-donate">Donate</a> </div> </div> <div id="header" class="is-hidden-mobile"> <a aria-hidden="true" tabindex="-1" 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</div> <div class="header-breadcrumbs-mobile"> <strong>arXiv:2411.10798</strong> (eess) </div> <link rel="stylesheet" type="text/css" href="/static/base/1.0.1/css/abs.css"> <div id="content-inner"> <div id="abs"> <span class="error" style="border: 2px solid grey">This paper has been withdrawn by Long Peng</span> <div class="dateline"> [Submitted on 16 Nov 2024 (<a href="https://arxiv.org/abs/2411.10798v1">v1</a>), last revised 21 Nov 2024 (this version, v2)]</div> <h1 class="title mathjax"><span class="descriptor">Title:</span>Unveiling Hidden Details: A RAW Data-Enhanced Paradigm for Real-World Super-Resolution</h1> <div class="authors"><span class="descriptor">Authors:</span><a href="https://arxiv.org/search/eess?searchtype=author&query=Peng,+L" rel="nofollow">Long Peng</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Li,+W" rel="nofollow">Wenbo Li</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Guo,+J" rel="nofollow">Jiaming Guo</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Di,+X" rel="nofollow">Xin Di</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Sun,+H" rel="nofollow">Haoze Sun</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Li,+Y" rel="nofollow">Yong Li</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Pei,+R" rel="nofollow">Renjing Pei</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Wang,+Y" rel="nofollow">Yang Wang</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Cao,+Y" rel="nofollow">Yang Cao</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Zha,+Z" rel="nofollow">Zheng-Jun Zha</a></div> <div id="download-button-info" hidden>View a PDF of the paper titled Unveiling Hidden Details: A RAW Data-Enhanced Paradigm for Real-World Super-Resolution, by Long Peng and 9 other authors</div> <a aria-describedby="download-button-info" class="mobile-submission-download mobile-download-grey" href="#other">No PDF available, click to view other formats</a> <blockquote class="abstract mathjax"> <span class="descriptor">Abstract:</span>Real-world image super-resolution (Real SR) aims to generate high-fidelity, detail-rich high-resolution (HR) images from low-resolution (LR) counterparts. Existing Real SR methods primarily focus on generating details from the LR RGB domain, often leading to a lack of richness or fidelity in fine details. In this paper, we pioneer the use of details hidden in RAW data to complement existing RGB-only methods, yielding superior outputs. We argue that key image processing steps in Image Signal Processing, such as denoising and demosaicing, inherently result in the loss of fine details in LR images, making LR RAW a valuable information source. To validate this, we present RealSR-RAW, a comprehensive dataset comprising over 10,000 pairs with LR and HR RGB images, along with corresponding LR RAW, captured across multiple smartphones under varying focal lengths and diverse scenes. Additionally, we propose a novel, general RAW adapter to efficiently integrate LR RAW data into existing CNNs, Transformers, and Diffusion-based Real SR models by suppressing the noise contained in LR RAW and aligning its distribution. Extensive experiments demonstrate that incorporating RAW data significantly enhances detail recovery and improves Real SR performance across ten evaluation metrics, including both fidelity and perception-oriented metrics. Our findings open a new direction for the Real SR task, with the dataset and code will be made available to support future research. </blockquote> <!--CONTEXT--> <div class="metatable"> <table summary="Additional metadata"> <tr> <td class="tablecell label">Comments:</td> <td class="tablecell comments mathjax"><em style="color: unset">We sincerely apologize, but due to some commercial confidentiality agreements related to the report, we have decided to withdraw the submission for now and will resubmit after making the necessary revisions</em></td> </tr> <tr> <td class="tablecell label">Subjects:</td> <td class="tablecell subjects"> <span class="primary-subject">Image and Video Processing (eess.IV)</span>; Computer Vision and Pattern Recognition (cs.CV)</td> </tr><tr> <td class="tablecell label">Cite as:</td> <td class="tablecell arxivid"><span class="arxivid"><a href="https://arxiv.org/abs/2411.10798">arXiv:2411.10798</a> [eess.IV]</span></td> </tr> <tr> <td class="tablecell label"> </td> <td class="tablecell arxividv">(or <span class="arxivid"> <a href="https://arxiv.org/abs/2411.10798v2">arXiv:2411.10798v2</a> [eess.IV]</span> for this version) </td> </tr> <tr> <td class="tablecell label"> </td> <td class="tablecell arxivdoi"> <a href="https://doi.org/10.48550/arXiv.2411.10798" id="arxiv-doi-link">https://doi.org/10.48550/arXiv.2411.10798</a><div class="button-and-tooltip"> <button class="more-info" aria-describedby="more-info-desc-1"> <svg height="15" role="presentation" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><path fill="currentColor" d="M256 8C119.043 8 8 119.083 8 256c0 136.997 111.043 248 248 248s248-111.003 248-248C504 119.083 392.957 8 256 8zm0 110c23.196 0 42 18.804 42 42s-18.804 42-42 42-42-18.804-42-42 18.804-42 42-42zm56 254c0 6.627-5.373 12-12 12h-88c-6.627 0-12-5.373-12-12v-24c0-6.627 5.373-12 12-12h12v-64h-12c-6.627 0-12-5.373-12-12v-24c0-6.627 5.373-12 12-12h64c6.627 0 12 5.373 12 12v100h12c6.627 0 12 5.373 12 12v24z" 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