{"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/videnn-deep-blind-video-denoising","title":"ViDeNN: Deep Blind Video Denoising","arxiv_id":"1904.10898","date":"2019-04-24","proceeding":null,"authors":["Michele Claus","Jan van Gemert"],"abstract":"We propose ViDeNN: a CNN for Video Denoising without prior knowledge on the\nnoise distribution (blind denoising). The CNN architecture uses a combination\nof spatial and temporal filtering, learning to spatially denoise the frames\nfirst and at the same time how to combine their temporal information, handling\nobjects motion, brightness changes, low-light conditions and temporal\ninconsistencies. We demonstrate the importance of the data used for CNNs\ntraining, creating for this purpose a specific dataset for low-light\nconditions. We test ViDeNN on common benchmarks and on self-collected data,\nachieving good results comparable with the state-of-the-art.","url_abs":"http://arxiv.org/abs/1904.10898v1","url_pdf":"http://arxiv.org/pdf/1904.10898v1.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":"videnn-deep-blind-video-denoising","repo_url":"https://github.com/clausmichele/ViDeNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"video-denoising","task_name":"Video Denoising"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/color-image-denoising-on-cbsd68-sigma10","task":"Color Image Denoising","dataset":"CBSD68 sigma10","model":"Spatial-CNN","rank_in_archive_order":2,"of":2,"metrics":{"PSNR":"35.92"},"uses_additional_data":false},{"leaderboard":"/sota/color-image-denoising-on-cbsd68-sigma15","task":"Color Image Denoising","dataset":"CBSD68 sigma15","model":"Spatial-CNN","rank_in_archive_order":10,"of":10,"metrics":{"PSNR":"33.66"},"uses_additional_data":false},{"leaderboard":"/sota/color-image-denoising-on-cbsd68-sigma25","task":"Color Image Denoising","dataset":"CBSD68 sigma25","model":"Spatial-CNN","rank_in_archive_order":8,"of":9,"metrics":{"PSNR":"30.99"},"uses_additional_data":false},{"leaderboard":"/sota/color-image-denoising-on-cbsd68-sigma35","task":"Color Image Denoising","dataset":"CBSD68 sigma35","model":"Spatial-CNN","rank_in_archive_order":4,"of":6,"metrics":{"PSNR":"29.34"},"uses_additional_data":false},{"leaderboard":"/sota/color-image-denoising-on-cbsd68-sigma5","task":"Color Image Denoising","dataset":"CBSD68 sigma5","model":"Spatial-CNN","rank_in_archive_order":3,"of":3,"metrics":{"PSNR":"39.73"},"uses_additional_data":false},{"leaderboard":"/sota/color-image-denoising-on-cbsd68-sigma50","task":"Color Image Denoising","dataset":"CBSD68 sigma50","model":"Spatial-CNN","rank_in_archive_order":15,"of":18,"metrics":{"PSNR":"27.63"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.10898","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}