{"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/deep-white-balance-editing","title":"Deep White-Balance Editing","arxiv_id":"2004.01354","date":"2020-04-03","proceeding":"CVPR 2020 6","authors":["Mahmoud Afifi","Michael S. Brown"],"abstract":"We introduce a deep learning approach to realistically edit an sRGB image's white balance. Cameras capture sensor images that are rendered by their integrated signal processor (ISP) to a standard RGB (sRGB) color space encoding. The ISP rendering begins with a white-balance procedure that is used to remove the color cast of the scene's illumination. The ISP then applies a series of nonlinear color manipulations to enhance the visual quality of the final sRGB image. Recent work by [3] showed that sRGB images that were rendered with the incorrect white balance cannot be easily corrected due to the ISP's nonlinear rendering. The work in [3] proposed a k-nearest neighbor (KNN) solution based on tens of thousands of image pairs. We propose to solve this problem with a deep neural network (DNN) architecture trained in an end-to-end manner to learn the correct white balance. Our DNN maps an input image to two additional white-balance settings corresponding to indoor and outdoor illuminations. Our solution not only is more accurate than the KNN approach in terms of correcting a wrong white-balance setting but also provides the user the freedom to edit the white balance in the sRGB image to other illumination settings.","url_abs":"https://arxiv.org/abs/2004.01354v1","url_pdf":"https://arxiv.org/pdf/2004.01354v1.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":"deep-white-balance-editing","repo_url":"https://github.com/mahmoudnafifi/Deep_White_Balance","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"deep-white-balance-editing","repo_url":"https://github.com/mahmoudnafifi/Image_recoloring","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"deep-white-balance-editing","repo_url":"https://github.com/mahmoudnafifi/WB_color_augmenter","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"deep-white-balance-editing","repo_url":"https://github.com/mahmoudnafifi/WB_sRGB","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2004.01354","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}