  Aiming to restore the original intensity of shadow regions in an image and
make them compatible with the remaining non-shadow regions without a trace,
shadow removal is a very challenging problem that benefits many downstream
image/video-related tasks. Recently, transformers have shown their strong
capability in various applications by capturing global pixel interactions and
this capability is highly desirable in shadow removal. However, applying
transformers to promote shadow removal is non-trivial for the following two
reasons: 1) The patchify operation is not suitable for shadow removal due to
irregular shadow shapes; 2) shadow removal only needs one-way interaction from
the non-shadow region to the shadow region instead of the common two-way
interactions among all pixels in the image. In this paper, we propose a novel
cross-region transformer, namely CRFormer, for shadow removal which differs
from existing transformers by only considering the pixel interactions from the
non-shadow region to the shadow region without splitting images into patches.
This is achieved by a carefully designed region-aware cross-attention operation
that can aggregate the recovered shadow region features conditioned on the
non-shadow region features. Extensive experiments on ISTD, AISTD, SRD, and
Video Shadow Removal datasets demonstrate the superiority of our method
compared to other state-of-the-art methods.
