{"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/neural-image-re-exposure","title":"Neural Image Re-Exposure","arxiv_id":"2305.13593","date":"2023-05-23","proceeding":null,"authors":["Xinyu Zhang","Hefei Huang","Xu Jia","Dong Wang","Huchuan Lu"],"abstract":"The shutter strategy applied to the photo-shooting process has a significant influence on the quality of the captured photograph. An improper shutter may lead to a blurry image, video discontinuity, or rolling shutter artifact. Existing works try to provide an independent solution for each issue. In this work, we aim to re-expose the captured photo in post-processing to provide a more flexible way of addressing those issues within a unified framework. Specifically, we propose a neural network-based image re-exposure framework. It consists of an encoder for visual latent space construction, a re-exposure module for aggregating information to neural film with a desired shutter strategy, and a decoder for 'developing' neural film into a desired image. To compensate for information confusion and missing frames, event streams, which can capture almost continuous brightness changes, are leveraged in computing visual latent content. Both self-attention layers and cross-attention layers are employed in the re-exposure module to promote interaction between neural film and visual latent content and information aggregation to neural film. The proposed unified image re-exposure framework is evaluated on several shutter-related image recovery tasks and performs favorably against independent state-of-the-art methods.","url_abs":"https://arxiv.org/abs/2305.13593v1","url_pdf":"https://arxiv.org/pdf/2305.13593v1.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":"neural-image-re-exposure","repo_url":"https://github.com/zhangxydlut/Neural-Image-Re-Exposure","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"deblurring","task_name":"Deblurring"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"joint-deblur-and-frame-interpolation","task_name":"Joint Deblur and Frame Interpolation"},{"task_slug":"joint-deblur-and-unrolling","task_name":"Joint Deblur and Unrolling"},{"task_slug":"unrolling","task_name":"Rolling Shutter Correction"},{"task_slug":"video-enhancement","task_name":"Video Enhancement"},{"task_slug":"video-frame-interpolation","task_name":"Video Frame Interpolation"},{"task_slug":"video-restoration","task_name":"Video Restoration"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/deblurring-on-gopro","task":"Deblurring","dataset":"GoPro","model":"NIRE","rank_in_archive_order":5,"of":56,"metrics":{"PSNR":"35.03","SSIM":"0.973"},"uses_additional_data":true}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2305.13593","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}