  Recent deep learning methods have achieved promising results in image shadow
removal. However, their restored images still suffer from unsatisfactory
boundary artifacts, due to the lack of degradation prior embedding and the
deficiency in modeling capacity. Our work addresses these issues by proposing a
unified diffusion framework that integrates both the image and degradation
priors for highly effective shadow removal. In detail, we first propose a
shadow degradation model, which inspires us to build a novel unrolling
diffusion model, dubbed ShandowDiffusion. It remarkably improves the model's
capacity in shadow removal via progressively refining the desired output with
both degradation prior and diffusive generative prior, which by nature can
serve as a new strong baseline for image restoration. Furthermore,
ShadowDiffusion progressively refines the estimated shadow mask as an auxiliary
task of the diffusion generator, which leads to more accurate and robust
shadow-free image generation. We conduct extensive experiments on three popular
public datasets, including ISTD, ISTD+, and SRD, to validate our method's
effectiveness. Compared to the state-of-the-art methods, our model achieves a
significant improvement in terms of PSNR, increasing from 31.69dB to 34.73dB
over SRD dataset.
