  The challenges surrounding the application of image shadow removal to
real-world images and not just constrained datasets like ISTD/SRD have
highlighted an urgent need for zero-shot learning in this field. In this study,
we innovatively adapted the SAM (Segment anything model) for shadow removal by
introducing SAM-helps-Shadow, effectively integrating shadow detection and
removal into a single stage. Our approach utilized the model's detection
results as a potent prior for facilitating shadow detection, followed by shadow
removal using a second-order deep unfolding network. The source code of
SAM-helps-Shadow can be obtained from
https://github.com/zhangbaijin/SAM-helps-Shadow.
