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Our loss function includes\ntwo perceptual losses: a feature loss from a visual perception network, and an\nadversarial loss that encodes characteristics of images in the transmission\nlayers. We also propose a novel exclusion loss that enforces pixel-level layer\nseparation. We create a dataset of real-world images with reflection and\ncorresponding ground-truth transmission layers for quantitative evaluation and\nmodel training. We validate our method through comprehensive quantitative\nexperiments and show that our approach outperforms state-of-the-art reflection\nremoval methods in PSNR, SSIM, and perceptual user study. We also extend our\nmethod to two other image enhancement tasks to demonstrate the generality of\nour approach.","url_abs":"http://arxiv.org/abs/1806.05376v1","url_pdf":"http://arxiv.org/pdf/1806.05376v1.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":"single-image-reflection-separation-with","repo_url":"https://github.com/ceciliavision/perceptual-reflection-removal","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"single-image-reflection-separation-with","repo_url":"https://github.com/mducducd/Shadow-Removal","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"single-image-reflection-separation-with","repo_url":"https://github.com/mducducd/ghost-free-shadow-removal","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"image-enhancement","task_name":"Image Enhancement"},{"task_slug":"reflection-removal","task_name":"Reflection Removal"},{"task_slug":"ssim","task_name":"SSIM"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.05376","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.05376"}},"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. 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