{"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/region-aware-exposure-consistency-network-for","title":"Region-Aware Exposure Consistency Network for Mixed Exposure Correction","arxiv_id":"2402.18217","date":"2024-02-28","proceeding":null,"authors":["Jin Liu","Huiyuan Fu","Chuanming Wang","Huadong Ma"],"abstract":"Exposure correction aims to enhance images suffering from improper exposure to achieve satisfactory visual effects. Despite recent progress, existing methods generally mitigate either overexposure or underexposure in input images, and they still struggle to handle images with mixed exposure, i.e., one image incorporates both overexposed and underexposed regions. The mixed exposure distribution is non-uniform and leads to varying representation, which makes it challenging to address in a unified process. In this paper, we introduce an effective Region-aware Exposure Correction Network (RECNet) that can handle mixed exposure by adaptively learning and bridging different regional exposure representations. Specifically, to address the challenge posed by mixed exposure disparities, we develop a region-aware de-exposure module that effectively translates regional features of mixed exposure scenarios into an exposure-invariant feature space. Simultaneously, as de-exposure operation inevitably reduces discriminative information, we introduce a mixed-scale restoration unit that integrates exposure-invariant features and unprocessed features to recover local information. To further achieve a uniform exposure distribution in the global image, we propose an exposure contrastive regularization strategy under the constraints of intra-regional exposure consistency and inter-regional exposure continuity. Extensive experiments are conducted on various datasets, and the experimental results demonstrate the superiority and generalization of our proposed method. The code is released at: https://github.com/kravrolens/RECNet.","url_abs":"https://arxiv.org/abs/2402.18217v1","url_pdf":"https://arxiv.org/pdf/2402.18217v1.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":"region-aware-exposure-consistency-network-for","repo_url":"https://github.com/kravrolens/recnet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"exposure-correction","task_name":"Exposure Correction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2402.18217","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.18217"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/kravrolens/RECNet","reach":null}],"summary":{"ran":4,"unverified":5},"by_repo_kind":{"official":{"samples":9,"ran":4,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"4b9ee0fe03ff2466","entry":"CALayer","repo":"kravrolens/RECNet","repo_kind":"official","path":"models/archs/EnhanceN_arch.py","file_url":"https://github.com/kravrolens/RECNet/blob/HEAD/models/archs/EnhanceN_arch.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"4b9ee0fe03ff2466"}},{"code_sha256_prefix":"1cfaf0929e549c21","entry":"IlluNet","repo":"kravrolens/RECNet","repo_kind":"official","path":"models/archs/EnhanceN_arch.py","file_url":"https://github.com/kravrolens/RECNet/blob/HEAD/models/archs/EnhanceN_arch.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"1cfaf0929e549c21"}},{"code_sha256_prefix":"2c19d743c04fb193","entry":"MCSA","repo":"kravrolens/RECNet","repo_kind":"official","path":"models/archs/EnhanceN_arch.py","file_url":"https://github.com/kravrolens/RECNet/blob/HEAD/models/archs/EnhanceN_arch.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"2c19d743c04fb193"}},{"code_sha256_prefix":"a3fe57de5f47b21c","entry":"Refine","repo":"kravrolens/RECNet","repo_kind":"official","path":"models/archs/EnhanceN_arch.py","file_url":"https://github.com/kravrolens/RECNet/blob/HEAD/models/archs/EnhanceN_arch.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"a3fe57de5f47b21c"}},{"code_sha256_prefix":"9c5edab6b3de4dac","entry":"DualBlock","repo":"kravrolens/RECNet","repo_kind":"official","path":"models/archs/EnhanceN_arch.py","file_url":"https://github.com/kravrolens/RECNet/blob/HEAD/models/archs/EnhanceN_arch.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"9c5edab6b3de4dac"}},{"code_sha256_prefix":"6bc1d9b72150e1ac","entry":"DualProcess","repo":"kravrolens/RECNet","repo_kind":"official","path":"models/archs/EnhanceN_arch.py","file_url":"https://github.com/kravrolens/RECNet/blob/HEAD/models/archs/EnhanceN_arch.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"6bc1d9b72150e1ac"}},{"code_sha256_prefix":"86cfd71bfb543cc0","entry":"GaussianBlur","repo":"kravrolens/RECNet","repo_kind":"official","path":"models/archs/EnhanceN_arch.py","file_url":"https://github.com/kravrolens/RECNet/blob/HEAD/models/archs/EnhanceN_arch.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"86cfd71bfb543cc0"}},{"code_sha256_prefix":"ebfe739d3e1d07e6","entry":"InteractNet","repo":"kravrolens/RECNet","repo_kind":"official","path":"models/archs/EnhanceN_arch.py","file_url":"https://github.com/kravrolens/RECNet/blob/HEAD/models/archs/EnhanceN_arch.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"ebfe739d3e1d07e6"}},{"code_sha256_prefix":"8ca1841ad1d20ef9","entry":"RIN","repo":"kravrolens/RECNet","repo_kind":"official","path":"models/archs/EnhanceN_arch.py","file_url":"https://github.com/kravrolens/RECNet/blob/HEAD/models/archs/EnhanceN_arch.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"8ca1841ad1d20ef9"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}