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To solve these two degradation issues, we present an underwater image enhancement network via medium transmission-guided multi-color space embedding, called Ucolor. Concretely, we first propose a multi-color space encoder network, which enriches the diversity of feature representations by incorporating the characteristics of different color spaces into a unified structure. Coupled with an attention mechanism, the most discriminative features extracted from multiple color spaces are adaptively integrated and highlighted. Inspired by underwater imaging physical models, we design a medium transmission (indicating the percentage of the scene radiance reaching the camera)-guided decoder network to enhance the response of the network towards quality-degraded regions. As a result, our network can effectively improve the visual quality of underwater images by exploiting multiple color spaces embedding and the advantages of both physical model-based and learning-based methods. Extensive experiments demonstrate that our Ucolor achieves superior performance against state-of-the-art methods in terms of both visual quality and quantitative metrics.","url_abs":"https://arxiv.org/abs/2104.13015v1","url_pdf":"https://arxiv.org/pdf/2104.13015v1.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":"underwater-image-enhancement-via-medium","repo_url":"https://github.com/59Kkk/pytorch_Ucolor_lcy","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"underwater-image-enhancement-via-medium","repo_url":"https://github.com/CV-Reimplementation/Ucolor-Reimplementation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"underwater-image-enhancement-via-medium","repo_url":"https://github.com/Li-Chongyi/Ucolor","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"underwater-image-enhancement-via-medium","repo_url":"https://github.com/MindCode-4/code-10/tree/main/UColor","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"underwater-image-enhancement-via-medium","repo_url":"https://github.com/MindCode-4/code-14/tree/main/UColor","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"underwater-image-enhancement-via-medium","repo_url":"https://github.com/kingcong/UColor-MindSpore-","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":{"status":"gone","observed_at":"2026-09-17","how":"tree_404+repo_404"}},{"paper_slug":"underwater-image-enhancement-via-medium","repo_url":"https://github.com/mindspore-ai/contrib/tree/master/papers/UColor","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"image-enhancement","task_name":"Image Enhancement"},{"task_slug":"underwater-image-restoration","task_name":"Underwater Image Restoration"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/underwater-image-restoration-on-lsui","task":"Underwater Image Restoration","dataset":"LSUI","model":"Ucolor","rank_in_archive_order":3,"of":6,"metrics":{"PSNR":"22.91"},"uses_additional_data":true}],"syntology":{"syntology_url":"https://syntology.ai/paper/2104.13015","atlas_url":"https://app.syntology.ai/?focus=2104.13015","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.13015"}},"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. 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