  In this paper, we analyze and discuss ShadowFormer in preparation for the
NTIRE2023 Shadow Removal Challenge [1], implementing five key improvements:
image alignment, the introduction of a perceptual quality loss function, the
semi-automatic annotation for shadow detection, joint learning of shadow
detection and removal, and the introduction of new data augmentation technique
"CutShadow" for shadow removal. Our method achieved scores of 0.196 (3rd out of
19) in LPIPS and 7.44 (4th out of 19) in the Mean Opinion Score (MOS).
