  We propose Diff-Shadow, a global-guided diffusion model for high-quality
shadow removal. Previous transformer-based approaches can utilize global
information to relate shadow and non-shadow regions but are limited in their
synthesis ability and recover images with obvious boundaries. In contrast,
diffusion-based methods can generate better content but ignore global
information, resulting in inconsistent illumination. In this work, we combine
the advantages of diffusion models and global guidance to realize shadow-free
restoration. Specifically, we propose a parallel UNets architecture: 1) the
local branch performs the patch-based noise estimation in the diffusion
process, and 2) the global branch recovers the low-resolution shadow-free
images. A Reweight Cross Attention (RCA) module is designed to integrate global
contextural information of non-shadow regions into the local branch. We further
design a Global-guided Sampling Strategy (GSS) that mitigates patch boundary
issues and ensures consistent illumination across shaded and unshaded regions
in the recovered image. Comprehensive experiments on three publicly standard
datasets ISTD, ISTD+, and SRD have demonstrated the effectiveness of
Diff-Shadow. Compared to state-of-the-art methods, our method achieves a
significant improvement in terms of PSNR, increasing from 32.33dB to 33.69dB on
the SRD dataset. Codes will be released.
