NTIRE 2023 Image Shadow Removal Challenge Technical Report: Team IIM_TTI
2024-03-13Code Available0· sign in to hype
Yuki Kondo, Riku Miyata, Fuma Yasue, Taito Naruki, Norimichi Ukita
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- github.com/Yuki-11/NTIRE2023_ShadowRemoval_IIM_TTIOfficialIn paperpytorch★ 5
Abstract
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).