NTIRE 2023 Image Shadow Removal Challenge Technical Report: Team IIM_TTI

1 code implementation • 13 Mar 2024 • Yuki Kondo, Riku Miyata, Fuma Yasue, Taito Naruki, Norimichi Ukita

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.

Data Augmentation Image Shadow Removal +2