{"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/dvdnet-a-fast-network-for-deep-video","title":"DVDnet: A Fast Network for Deep Video Denoising","arxiv_id":"1906.11890","date":"2019-06-04","proceeding":null,"authors":["Matias Tassano","Julie Delon","Thomas Veit"],"abstract":"In this paper, we propose a state-of-the-art video denoising algorithm based on a convolutional neural network architecture. Previous neural network based approaches to video denoising have been unsuccessful as their performance cannot compete with the performance of patch-based methods. However, our approach outperforms other patch-based competitors with significantly lower computing times. In contrast to other existing neural network denoisers, our algorithm exhibits several desirable properties such as a small memory footprint, and the ability to handle a wide range of noise levels with a single network model. The combination between its denoising performance and lower computational load makes this algorithm attractive for practical denoising applications. We compare our method with different state-of-art algorithms, both visually and with respect to objective quality metrics. The experiments show that our algorithm compares favorably to other state-of-art methods. Video examples, code and models are publicly available at \\url{https://github.com/m-tassano/dvdnet}.","url_abs":"https://arxiv.org/abs/1906.11890v1","url_pdf":"https://arxiv.org/pdf/1906.11890v1.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":"dvdnet-a-fast-network-for-deep-video","repo_url":"https://github.com/m-tassano/dvdnet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"video-denoising","task_name":"Video Denoising"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-denoising-on-davis-sigma10","task":"Video Denoising","dataset":"DAVIS sigma10","model":"DVDnet","rank_in_archive_order":6,"of":6,"metrics":{"PSNR":"38.13"},"uses_additional_data":false},{"leaderboard":"/sota/video-denoising-on-davis-sigma20","task":"Video Denoising","dataset":"DAVIS sigma20","model":"DVDnet","rank_in_archive_order":6,"of":7,"metrics":{"PSNR":"35.7"},"uses_additional_data":false},{"leaderboard":"/sota/video-denoising-on-davis-sigma30","task":"Video Denoising","dataset":"DAVIS sigma30","model":"DVDnet","rank_in_archive_order":5,"of":7,"metrics":{"PSNR":"34.08"},"uses_additional_data":false},{"leaderboard":"/sota/video-denoising-on-davis-sigma40","task":"Video Denoising","dataset":"DAVIS sigma40","model":"DVDnet","rank_in_archive_order":6,"of":8,"metrics":{"PSNR":"32.86"},"uses_additional_data":false},{"leaderboard":"/sota/video-denoising-on-davis-sigma50","task":"Video Denoising","dataset":"DAVIS sigma50","model":"DVDnet","rank_in_archive_order":6,"of":8,"metrics":{"PSNR":"31.85"},"uses_additional_data":false},{"leaderboard":"/sota/video-denoising-on-set8-sigma10","task":"Video Denoising","dataset":"Set8 sigma10","model":"DVDnet","rank_in_archive_order":6,"of":6,"metrics":{"PSNR":"36.08"},"uses_additional_data":false},{"leaderboard":"/sota/video-denoising-on-set8-sigma20","task":"Video Denoising","dataset":"Set8 sigma20","model":"DVDnet","rank_in_archive_order":5,"of":7,"metrics":{"PSNR":"33.49"},"uses_additional_data":false},{"leaderboard":"/sota/video-denoising-on-set8-sigma30","task":"Video Denoising","dataset":"Set8 sigma30","model":"DVDnet","rank_in_archive_order":6,"of":7,"metrics":{"PSNR":"31.79"},"uses_additional_data":false},{"leaderboard":"/sota/video-denoising-on-set8-sigma40","task":"Video Denoising","dataset":"Set8 sigma40","model":"DVDnet","rank_in_archive_order":7,"of":8,"metrics":{"PSNR":"30.55"},"uses_additional_data":false},{"leaderboard":"/sota/video-denoising-on-set8-sigma50","task":"Video Denoising","dataset":"Set8 sigma50","model":"DVDnet","rank_in_archive_order":8,"of":9,"metrics":{"PSNR":"29.56"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1906.11890","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}