{"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/learning-deformable-kernels-for-image-and","title":"Learning Deformable Kernels for Image and Video Denoising","arxiv_id":"1904.06903","date":"2019-04-15","proceeding":null,"authors":["Xiangyu Xu","Muchen Li","Wenxiu Sun"],"abstract":"Most of the classical denoising methods restore clear results by selecting\nand averaging pixels in the noisy input. Instead of relying on hand-crafted\nselecting and averaging strategies, we propose to explicitly learn this process\nwith deep neural networks. Specifically, we propose deformable 2D kernels for\nimage denoising where the sampling locations and kernel weights are both\nlearned. The proposed kernel naturally adapts to image structures and could\neffectively reduce the oversmoothing artifacts. Furthermore, we develop 3D\ndeformable kernels for video denoising to more efficiently sample pixels across\nthe spatial-temporal space. Our method is able to solve the misalignment issues\nof large motion from dynamic scenes. For better training our video denoising\nmodel, we introduce the trilinear sampler and a new regularization term. We\ndemonstrate that the proposed method performs favorably against the\nstate-of-the-art image and video denoising approaches on both synthetic and\nreal-world data.","url_abs":"http://arxiv.org/abs/1904.06903v1","url_pdf":"http://arxiv.org/pdf/1904.06903v1.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":"learning-deformable-kernels-for-image-and","repo_url":"https://github.com/jojo23333/STPAN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"learning-deformable-kernels-for-image-and","repo_url":"https://github.com/z-bingo/Deformable-Kernels-For-Video-Denoising","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"image-denoising","task_name":"Image Denoising"},{"task_slug":"video-denoising","task_name":"Video Denoising"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1904.06903","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}