{"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/lednet-joint-low-light-enhancement-and","title":"LEDNet: Joint Low-light Enhancement and Deblurring in the Dark","arxiv_id":"2202.03373","date":"2022-02-07","proceeding":null,"authors":["Shangchen Zhou","Chongyi Li","Chen Change Loy"],"abstract":"Night photography typically suffers from both low light and blurring issues due to the dim environment and the common use of long exposure. While existing light enhancement and deblurring methods could deal with each problem individually, a cascade of such methods cannot work harmoniously to cope well with joint degradation of visibility and textures. Training an end-to-end network is also infeasible as no paired data is available to characterize the coexistence of low light and blurs. We address the problem by introducing a novel data synthesis pipeline that models realistic low-light blurring degradations. With the pipeline, we present the first large-scale dataset for joint low-light enhancement and deblurring. The dataset, LOL-Blur, contains 12,000 low-blur/normal-sharp pairs with diverse darkness and motion blurs in different scenarios. We further present an effective network, named LEDNet, to perform joint low-light enhancement and deblurring. Our network is unique as it is specially designed to consider the synergy between the two inter-connected tasks. Both the proposed dataset and network provide a foundation for this challenging joint task. Extensive experiments demonstrate the effectiveness of our method on both synthetic and real-world datasets.","url_abs":"https://arxiv.org/abs/2202.03373v2","url_pdf":"https://arxiv.org/pdf/2202.03373v2.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":"lednet-joint-low-light-enhancement-and","repo_url":"https://github.com/sczhou/LEDNet","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"deblurring","task_name":"Deblurring"},{"task_slug":"low-light-image-enhancement","task_name":"Low-Light Image Enhancement"},{"task_slug":"low-light-image-deblurring-and-enhancement","task_name":"Low-light Image Deblurring and Enhancement"}],"methods":[],"datasets_introduced":[{"slug":"lol-blur","name":"LOL-Blur","full_name":"low-blur/high-sharp-scaled"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/low-light-image-enhancement-on-sony-total","task":"Low-Light Image Enhancement","dataset":"Sony-Total-Dark","model":"LEDNet","rank_in_archive_order":2,"of":3,"metrics":{"Average PSNR":"20.830","LPIPS":"0.471","SSIM":"0.648"},"uses_additional_data":false},{"leaderboard":"/sota/low-light-image-deblurring-and-enhancement-on","task":"Low-light Image Deblurring and Enhancement","dataset":"LOL-Blur","model":"LEDNet","rank_in_archive_order":2,"of":3,"metrics":{"Average PSNR":"25.271","LPIPS":"0.141","SSIM":"0.850"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2202.03373","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.03373"}},"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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