{"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/real-image-denoising-with-feature-attention","title":"Real Image Denoising with Feature Attention","arxiv_id":"1904.07396","date":"2019-04-16","proceeding":"ICCV 2019 10","authors":["Saeed Anwar","Nick Barnes"],"abstract":"Deep convolutional neural networks perform better on images containing spatially invariant noise (synthetic noise); however, their performance is limited on real-noisy photographs and requires multiple stage network modeling. To advance the practicability of denoising algorithms, this paper proposes a novel single-stage blind real image denoising network (RIDNet) by employing a modular architecture. We use a residual on the residual structure to ease the flow of low-frequency information and apply feature attention to exploit the channel dependencies. Furthermore, the evaluation in terms of quantitative metrics and visual quality on three synthetic and four real noisy datasets against 19 state-of-the-art algorithms demonstrate the superiority of our RIDNet.","url_abs":"https://arxiv.org/abs/1904.07396v2","url_pdf":"https://arxiv.org/pdf/1904.07396v2.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":"real-image-denoising-with-feature-attention","repo_url":"https://github.com/saeed-anwar/RIDNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"real-image-denoising-with-feature-attention","repo_url":"https://github.com/sunilbelde/Imagedenoising-dncnn-keras","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"real-image-denoising-with-feature-attention","repo_url":"https://github.com/sunilbelde/Imagedenoising-dncnn-ridnet-keras","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"color-image-denoising","task_name":"Color Image Denoising"},{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"image-denoising","task_name":"Image Denoising"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/color-image-denoising-on-bsd68-sigma15","task":"Color Image Denoising","dataset":"BSD68 sigma15","model":"RIDNet","rank_in_archive_order":1,"of":4,"metrics":{"PSNR":"34.01"},"uses_additional_data":false},{"leaderboard":"/sota/color-image-denoising-on-bsd68-sigma25","task":"Color Image Denoising","dataset":"BSD68 sigma25","model":"RIDNet","rank_in_archive_order":1,"of":4,"metrics":{"PSNR":"31.37"},"uses_additional_data":false},{"leaderboard":"/sota/color-image-denoising-on-cbsd68-sigma50","task":"Color Image Denoising","dataset":"CBSD68 sigma50","model":"RIDNet","rank_in_archive_order":9,"of":18,"metrics":{"PSNR":"28.14"},"uses_additional_data":false},{"leaderboard":"/sota/color-image-denoising-on-darmstadt-noise","task":"Color Image Denoising","dataset":"Darmstadt Noise Dataset","model":"RIDNet (blind)","rank_in_archive_order":3,"of":6,"metrics":{"PSNR (sRGB)":"39.23","SSIM (sRGB)":"0.9526"},"uses_additional_data":false},{"leaderboard":"/sota/grayscale-image-denoising-on-bsd68-sigma15","task":"Grayscale Image Denoising","dataset":"BSD68 sigma15","model":"RIDNet","rank_in_archive_order":9,"of":16,"metrics":{"PSNR":"31.81"},"uses_additional_data":false},{"leaderboard":"/sota/grayscale-image-denoising-on-bsd68-sigma25","task":"Grayscale Image Denoising","dataset":"BSD68 sigma25","model":"RIDNet","rank_in_archive_order":7,"of":16,"metrics":{"PSNR":"29.34"},"uses_additional_data":false},{"leaderboard":"/sota/grayscale-image-denoising-on-bsd68-sigma50","task":"Grayscale Image Denoising","dataset":"BSD68 sigma50","model":"RIDNet","rank_in_archive_order":8,"of":15,"metrics":{"PSNR":"26.4"},"uses_additional_data":false},{"leaderboard":"/sota/image-denoising-on-dnd","task":"Image Denoising","dataset":"DND","model":"RIDNet","rank_in_archive_order":15,"of":16,"metrics":{"PSNR (sRGB)":"39.26","SSIM (sRGB)":"0.953"},"uses_additional_data":true},{"leaderboard":"/sota/image-denoising-on-sidd","task":"Image Denoising","dataset":"SIDD","model":"RIDNet","rank_in_archive_order":19,"of":22,"metrics":{"PSNR (sRGB)":"38.71","SSIM (sRGB)":"0.951"},"uses_additional_data":true}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.07396","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}