PhaSR: Generalized Image Shadow Removal with Physically Aligned Priors
Shadow removal under diverse lighting requires disentangling illumination from intrinsic reflectance— a challenge when physical priors are misaligned.
We propose PhaSR with dual-level prior alignment: (1) PAN performs parameter-free illumination correction via Gray-world normalization and log-domain Retinex decomposition, suppressing chromatic bias. (2) GSRA extends differential attention to harmonize depth-derived geometry with DINO-v2 semantics, resolving modal conflicts across illumination conditions.
Experiments demonstrate competitive performance with lower complexity, generalizing to ambient lighting where traditional methods fail.
A multi-scale Transformer encoder-decoder integrates frozen DINO-v2 semantic features and DepthAnything-v2 geometric priors via GSRA's cross-modal differential attention (Arect = Asem - λ·Ageo).
PhaSR (Ours)
DenseSR
ShadowRefiner
StableShadowDiffusion
PhaSR (Ours)
DenseSR
ShadowRefiner
StableShadowDiffusion
PhaSR (Ours)
DenseSR
OmniSR
StableShadowDiffusion
PhaSR (Ours)
DenseSR
OmniSR
StableShadowDiffusion
PhaSR (Ours)
DenseSR
OmniSR
StableShadowDiffusion
PhaSR (Ours)
DenseSR
OmniSR
StableShadowDiffusion
@misc{lee2024phasr,
title={PhaSR: Generalized Image Shadow Removal with Physically Aligned Priors},
author={Lee, Chia-Ming and Lin, Yu-Fan and Hsiao, Yu-Jou and Jiang, Jin-Hui and Liu, Yu-Lun and Hsu, Chih-Chung},
year={2026},
eprint={2601.17470},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2601.17470},
}