PhaSR: Generalized Image Shadow Removal with Physically Aligned Priors
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911549456646144 |
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| author | Lee, Chia-Ming Lin, Yu-Fan Hsiao, Yu-Jou Jiang, Jin-Hui Liu, Yu-Lun Hsu, Chih-Chung |
| author_facet | Lee, Chia-Ming Lin, Yu-Fan Hsiao, Yu-Jou Jiang, Jin-Hui Liu, Yu-Lun Hsu, Chih-Chung |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17470 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PhaSR: Generalized Image Shadow Removal with Physically Aligned Priors Lee, Chia-Ming Lin, Yu-Fan Hsiao, Yu-Jou Jiang, Jin-Hui Liu, Yu-Lun Hsu, Chih-Chung Computer Vision and Pattern Recognition 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. |
| title | PhaSR: Generalized Image Shadow Removal with Physically Aligned Priors |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.17470 |