{"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/exposure-aware-dynamic-weighted-learning-for","title":"Exposure-Aware Dynamic Weighted Learning for Single-Shot HDR Imaging","arxiv_id":null,"date":"2022-10-23","proceeding":"European Conference on Computer Vision (ECCV) 2022 10","authors":["An Gia Vien","Chul Lee"],"abstract":"We propose a novel single-shot high dynamic range (HDR) imaging algorithm based on exposure-aware dynamic weighted learning, which reconstructs an HDR image from a spatially varying exposure (SVE) raw image. First, we recover poorly exposed pixels by developing a network that learns local dynamic filters to exploit local neighboring pixels across color channels. Second, we develop another network that combines only valid features in well-exposed regions by learning exposure-aware feature fusion. Third, we synthesize the raw radiance map by adaptively combining the outputs of the two networks that have different characteristics with complementary information. Finally, a full-color HDR image is obtained by interpolating missing color information. Experimental results show that the proposed algorithm significantly outperforms conventional algorithms on various datasets.","url_abs":"https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136670429.pdf","url_pdf":"https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136670429.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":"exposure-aware-dynamic-weighted-learning-for","repo_url":"https://github.com/viengiaan/EDWL","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"hdr-reconstruction","task_name":"HDR Reconstruction"},{"task_slug":"image-reconstruction","task_name":"Image Reconstruction"},{"task_slug":"single-shot-hdr-reconstruction","task_name":"Single-shot HDR Reconstruction"},{"task_slug":null,"task_name":"valid"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}