{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/wide-inference-network-for-image-denoising","title":"Wide Inference Network for Image Denoising via Learning Pixel-distribution Prior","arxiv_id":"1707.05414","date":"2017-07-17","proceeding":null,"authors":["Peng Liu","Ruogu Fang"],"abstract":"We explore an innovative strategy for image denoising by using convolutional\nneural networks (CNN) to learn similar pixel-distribution features from noisy\nimages. Many types of image noise follow a certain pixel-distribution in\ncommon, such as additive white Gaussian noise (AWGN). By increasing CNN's width\nwith larger reception fields and more channels in each layer, CNNs can reveal\nthe ability to extract more accurate pixel-distribution features. The key to\nour approach is a discovery that wider CNNs with more convolutions tend to\nlearn the similar pixel-distribution features, which reveals a new strategy to\nsolve low-level vision problems effectively that the inference mapping\nprimarily relies on the priors behind the noise property instead of deeper CNNs\nwith more stacked nonlinear layers. We evaluate our work, Wide inference\nNetworks (WIN), on AWGN and demonstrate that by learning pixel-distribution\nfeatures from images, WIN-based network consistently achieves significantly\nbetter performance than current state-of-the-art deep CNN-based methods in both\nquantitative and visual evaluations. \\textit{Code and models are available at\n\\url{https://github.com/cswin/WIN}}.","url_abs":"http://arxiv.org/abs/1707.05414v5","url_pdf":"http://arxiv.org/pdf/1707.05414v5.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"wide-inference-network-for-image-denoising","repo_url":"https://github.com/cswin/WIN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"wide-inference-network-for-image-denoising","repo_url":"https://github.com/shibuiwilliam/DeepLearningDenoise","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"image-denoising","task_name":"Image Denoising"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}