๐‘๐ž๐ฌ๐ž๐š๐ซ๐œ๐กไธจPhysics-Guided RL Algorithm for Image Dehazing by Liu Chang๐‘๐ž๐ฌ๐ž๐š๐ซ๐œ๐กไธจPhysics-Guided RL Algorithm for Image Dehazing by Liu Chang

๐‘๐ž๐ฌ๐ž๐š๐ซ๐œ๐กไธจPhysics-Guided RL Algorithm for Image Dehazing

Liu Chang

Liu Chang

AI Researcher & Co-author
๐Œ๐จ๐ญ๐ข๐ฏ๐š๐ญ๐ข๐จ๐ง & ๐‚๐ก๐š๐ฅ๐ฅ๐ž๐ง๐ ๐ž: Unpaired dehazing needs reliable restoration without costly diffusion sampling. For this, our work Dehaze-RL learns physical parameters, enhancement factors and spatial gating, guided by perceptual rewards.
๐‘๐ž๐ฌ๐ฎ๐ฅ๐ญ๐ฌ & ๐•๐ž๐ง๐ฎ๐ž๐ฌ: Improved restoration quality with a physics-grounded hybrid action space and multi-granularity rewards. Accepted at ECCV 2026.
๐‹๐ข๐ง๐ค๏ผšhttps://media.eventhosts.cc/Conferences/ECCV2026/pdfs/3070.pdf
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Posted Sep 15, 2026

Physics-guided reinforcement learning for image dehazing. Accepted at ECCV 2026.

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