PhaSR: Generalized Image Shadow Removal with Physically Aligned Priors

• URL: http://arxiv.org/abs/2601.17470v2
• Date: Sat, 31 Jan 2026 02:36:42 GMT
• Title: PhaSR: Generalized Image Shadow Removal with Physically Aligned Priors
• Authors: Chia-Ming Lee, Yu-Fan Lin, Yu-Jou Hsiao, Jing-Hui Jung, Yu-Lun Liu, Chih-Chung Hsu,
• Abstract summary: We propose PhaSR (Physically Aligned Shadow Removal), addressing this through dual-level prior alignment.<n>Experiments show competitive performance in shadow removal with lower complexity and generalization to ambient lighting.
• Score: 13.290464696196366
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Shadow removal under diverse lighting conditions requires disentangling illumination from intrinsic reflectance, a challenge compounded when physical priors are not properly aligned. We propose PhaSR (Physically Aligned Shadow Removal), addressing this through dual-level prior alignment to enable robust performance from single-light shadows to multi-source ambient lighting. First, Physically Aligned Normalization (PAN) performs closed-form illumination correction via Gray-world normalization, log-domain Retinex decomposition, and dynamic range recombination, suppressing chromatic bias. Second, Geometric-Semantic Rectification Attention (GSRA) extends differential attention to cross-modal alignment, harmonizing depth-derived geometry with DINO-v2 semantic embeddings to resolve modal conflicts under varying illumination. Experiments show competitive performance in shadow removal with lower complexity and generalization to ambient lighting where traditional methods fail under multi-source illumination. Our source code is available at https://github.com/ming053l/PhaSR.

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