{"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/neighbor2neighbor-self-supervised-denoising","title":"Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images","arxiv_id":"2101.02824","date":"2021-01-08","proceeding":"CVPR 2021 1","authors":["Tao Huang","Songjiang Li","Xu Jia","Huchuan Lu","Jianzhuang Liu"],"abstract":"In the last few years, image denoising has benefited a lot from the fast development of neural networks. However, the requirement of large amounts of noisy-clean image pairs for supervision limits the wide use of these models. Although there have been a few attempts in training an image denoising model with only single noisy images, existing self-supervised denoising approaches suffer from inefficient network training, loss of useful information, or dependence on noise modeling. In this paper, we present a very simple yet effective method named Neighbor2Neighbor to train an effective image denoising model with only noisy images. Firstly, a random neighbor sub-sampler is proposed for the generation of training image pairs. In detail, input and target used to train a network are images sub-sampled from the same noisy image, satisfying the requirement that paired pixels of paired images are neighbors and have very similar appearance with each other. Secondly, a denoising network is trained on sub-sampled training pairs generated in the first stage, with a proposed regularizer as additional loss for better performance. The proposed Neighbor2Neighbor framework is able to enjoy the progress of state-of-the-art supervised denoising networks in network architecture design. Moreover, it avoids heavy dependence on the assumption of the noise distribution. We explain our approach from a theoretical perspective and further validate it through extensive experiments, including synthetic experiments with different noise distributions in sRGB space and real-world experiments on a denoising benchmark dataset in raw-RGB space.","url_abs":"https://arxiv.org/abs/2101.02824v3","url_pdf":"https://arxiv.org/pdf/2101.02824v3.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":"neighbor2neighbor-self-supervised-denoising","repo_url":"https://github.com/TaoHuang2018/Neighbor2Neighbor","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"neighbor2neighbor-self-supervised-denoising","repo_url":"https://github.com/Wenchao-Du/Neighbor2Neighbor_Pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"neighbor2neighbor-self-supervised-denoising","repo_url":"https://github.com/neeraj3029/Ne2Ne-Image-Denoising","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"neighbor2neighbor-self-supervised-denoising","repo_url":"https://github.com/pminhtam/Neigh2Neigh","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"neighbor2neighbor-self-supervised-denoising","repo_url":"https://github.com/MS-Mind/MS-Code-08/tree/main/Neighbor2Neighbor","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"neighbor2neighbor-self-supervised-denoising","repo_url":"https://github.com/Mind23-2/MindCode-101/tree/main/Neighbor2Neighbor","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"neighbor2neighbor-self-supervised-denoising","repo_url":"https://github.com/Mind23-2/MindCode-3/tree/main/Neighbor2Neighbor","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"neighbor2neighbor-self-supervised-denoising","repo_url":"https://github.com/code-implementation1/Code6/tree/main/Neighbor2Neighbor","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"neighbor2neighbor-self-supervised-denoising","repo_url":"https://github.com/mindspore-ai/models/tree/master/official/cv/unet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"neighbor2neighbor-self-supervised-denoising","repo_url":"https://github.com/txyugood/Neighbor2Neighbor_Paddle","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"paddle","reach":{"status":"unanswered"}},{"paper_slug":"neighbor2neighbor-self-supervised-denoising","repo_url":"https://github.com/wwhio/megmodels","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"neighbor2neighbor-self-supervised-denoising","repo_url":"https://github.com/yangyucheng000/ms_cv/tree/main/Neighbor2Neighbor","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"image-denoising","task_name":"Image Denoising"},{"task_slug":"self-supervised-learning","task_name":"Self-Supervised Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2101.02824","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.02824"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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