{"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/color-shift-estimation-and-correction-for","title":"Color Shift Estimation-and-Correction for Image Enhancement","arxiv_id":"2405.17725","date":"2024-05-28","proceeding":"CVPR 2024 1","authors":["Yiyu Li","Ke Xu","Gerhard Petrus Hancke","Rynson W. H. Lau"],"abstract":"Images captured under sub-optimal illumination conditions may contain both over- and under-exposures. Current approaches mainly focus on adjusting image brightness, which may exacerbate the color tone distortion in under-exposed areas and fail to restore accurate colors in over-exposed regions. We observe that over- and under-exposed regions display opposite color tone distribution shifts with respect to each other, which may not be easily normalized in joint modeling as they usually do not have ``normal-exposed'' regions/pixels as reference. In this paper, we propose a novel method to enhance images with both over- and under-exposures by learning to estimate and correct such color shifts. Specifically, we first derive the color feature maps of the brightened and darkened versions of the input image via a UNet-based network, followed by a pseudo-normal feature generator to produce pseudo-normal color feature maps. We then propose a novel COlor Shift Estimation (COSE) module to estimate the color shifts between the derived brightened (or darkened) color feature maps and the pseudo-normal color feature maps. The COSE module corrects the estimated color shifts of the over- and under-exposed regions separately. We further propose a novel COlor MOdulation (COMO) module to modulate the separately corrected colors in the over- and under-exposed regions to produce the enhanced image. Comprehensive experiments show that our method outperforms existing approaches. Project webpage: https://github.com/yiyulics/CSEC.","url_abs":"https://arxiv.org/abs/2405.17725v2","url_pdf":"https://arxiv.org/pdf/2405.17725v2.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":"color-shift-estimation-and-correction-for","repo_url":"https://github.com/yiyulics/csec","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"exposure-correction","task_name":"Exposure Correction"},{"task_slug":"image-enhancement","task_name":"Image Enhancement"},{"task_slug":"low-light-image-enhancement","task_name":"Low-Light Image Enhancement"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-enhancement-on-exposure-errors","task":"Image Enhancement","dataset":"Exposure-Errors","model":"CSEC","rank_in_archive_order":2,"of":5,"metrics":{"PSNR":"22.728","SSIM":"0.863"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2405.17725","atlas_url":"https://app.syntology.ai/?focus=2405.17725","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.17725"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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