{"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/few-shot-domain-adaptation-for-low-light-raw","title":"Few-Shot Domain Adaptation for Low Light RAW Image Enhancement","arxiv_id":"2303.15528","date":"2023-03-27","proceeding":null,"authors":["K. Ram Prabhakar","Vishal Vinod","Nihar Ranjan Sahoo","R. Venkatesh Babu"],"abstract":"Enhancing practical low light raw images is a difficult task due to severe noise and color distortions from short exposure time and limited illumination. Despite the success of existing Convolutional Neural Network (CNN) based methods, their performance is not adaptable to different camera domains. In addition, such methods also require large datasets with short-exposure and corresponding long-exposure ground truth raw images for each camera domain, which is tedious to compile. To address this issue, we present a novel few-shot domain adaptation method to utilize the existing source camera labeled data with few labeled samples from the target camera to improve the target domain's enhancement quality in extreme low-light imaging. Our experiments show that only ten or fewer labeled samples from the target camera domain are sufficient to achieve similar or better enhancement performance than training a model with a large labeled target camera dataset. To support research in this direction, we also present a new low-light raw image dataset captured with a Nikon camera, comprising short-exposure and their corresponding long-exposure ground truth images.","url_abs":"https://arxiv.org/abs/2303.15528v1","url_pdf":"https://arxiv.org/pdf/2303.15528v1.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":"few-shot-domain-adaptation-for-low-light-raw","repo_url":"https://github.com/Vishal-V/FSDA-LowLight","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"image-enhancement","task_name":"Image Enhancement"},{"task_slug":"low-light-image-enhancement","task_name":"Low-Light Image Enhancement"}],"methods":[],"datasets_introduced":[{"slug":"nikon-camera-low-light-raw-image-dataset","name":"Nikon RAW Low Light","full_name":"Nikon Camera Low Light RAW Image Dataset"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/domain-adaptation-on-canon-raw-low-light","task":"Domain Adaptation","dataset":"Canon RAW Low Light","model":"FSDA-LL Sony -> Canon","rank_in_archive_order":1,"of":1,"metrics":{"PSNR":"33.22","SSIM":"0.896"},"uses_additional_data":false},{"leaderboard":"/sota/domain-adaptation-on-nikon-raw-low-light","task":"Domain Adaptation","dataset":"Nikon RAW Low Light","model":"FSDA-LL Sony -> Nikon","rank_in_archive_order":1,"of":1,"metrics":{"PSNR":"30.3","SSIM":"0.913"},"uses_additional_data":false},{"leaderboard":"/sota/low-light-image-enhancement-on-canon-raw-low","task":"Low-Light Image Enhancement","dataset":"Canon RAW Low Light","model":"FSDA-LL","rank_in_archive_order":1,"of":1,"metrics":{"PSNR":"33.22","SSIM":"0.896"},"uses_additional_data":false},{"leaderboard":"/sota/low-light-image-enhancement-on-nikon-raw-low","task":"Low-Light Image Enhancement","dataset":"Nikon RAW Low Light","model":"FSDAL-LL","rank_in_archive_order":1,"of":1,"metrics":{"PSNR":"30.3","SSIM":"0.913"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2303.15528","atlas_url":"https://app.syntology.ai/?focus=2303.15528","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.15528"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Vishal-V/FSDA-LowLight","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran":7,"unverified":2},"by_repo_kind":{"official":{"samples":9,"ran":7,"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":"fd04084240f82190","entry":"gaussian_filter","repo":"Vishal-V/FSDA-LowLight","repo_kind":"official","path":"pytorch_msssim/ssim.py","file_url":"https://github.com/Vishal-V/FSDA-LowLight/blob/HEAD/pytorch_msssim/ssim.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"fd04084240f82190"}},{"code_sha256_prefix":"9eed7a6520919cad","entry":"ms_ssim","repo":"Vishal-V/FSDA-LowLight","repo_kind":"official","path":"pytorch_msssim/ssim.py","file_url":"https://github.com/Vishal-V/FSDA-LowLight/blob/HEAD/pytorch_msssim/ssim.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"9eed7a6520919cad"}},{"code_sha256_prefix":"ff4b668dd744d4ee","entry":"pack_canon","repo":"Vishal-V/FSDA-LowLight","repo_kind":"official","path":"data.py","file_url":"https://github.com/Vishal-V/FSDA-LowLight/blob/HEAD/data.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"ff4b668dd744d4ee"}},{"code_sha256_prefix":"ed2e7fcecc58b95a","entry":"pack_nikon","repo":"Vishal-V/FSDA-LowLight","repo_kind":"official","path":"data.py","file_url":"https://github.com/Vishal-V/FSDA-LowLight/blob/HEAD/data.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"ed2e7fcecc58b95a"}},{"code_sha256_prefix":"9b4d83516ecea6fa","entry":"pack_raw","repo":"Vishal-V/FSDA-LowLight","repo_kind":"official","path":"data.py","file_url":"https://github.com/Vishal-V/FSDA-LowLight/blob/HEAD/data.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"9b4d83516ecea6fa"}},{"code_sha256_prefix":"587fd1d7356edfe3","entry":"rgb2gray","repo":"Vishal-V/FSDA-LowLight","repo_kind":"official","path":"train_sony_canon.py","file_url":"https://github.com/Vishal-V/FSDA-LowLight/blob/HEAD/train_sony_canon.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"587fd1d7356edfe3"}},{"code_sha256_prefix":"661a52e9d4de525c","entry":"ssim_grayscale","repo":"Vishal-V/FSDA-LowLight","repo_kind":"official","path":"train_sony_canon.py","file_url":"https://github.com/Vishal-V/FSDA-LowLight/blob/HEAD/train_sony_canon.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"661a52e9d4de525c"}},{"code_sha256_prefix":"5f017c5114d24631","entry":"cos_loss","repo":"Vishal-V/FSDA-LowLight","repo_kind":"official","path":"train_sony_canon.py","file_url":"https://github.com/Vishal-V/FSDA-LowLight/blob/HEAD/train_sony_canon.py","link_basis":"harvester_set","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":"5f017c5114d24631"}},{"code_sha256_prefix":"035c4938516d1fff","entry":"ssim","repo":"Vishal-V/FSDA-LowLight","repo_kind":"official","path":"pytorch_msssim/ssim.py","file_url":"https://github.com/Vishal-V/FSDA-LowLight/blob/HEAD/pytorch_msssim/ssim.py","link_basis":"harvester_set","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":"035c4938516d1fff"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}