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NTIRE 2023 Image Shadow Removal Challenge Technical Report: Team IIM_TTI
March 15, 2024, 4:45 a.m. | Yuki Kondo, Riku Miyata, Fuma Yasue, Taito Naruki, Norimichi Ukita
cs.CV updates on arXiv.org arxiv.org
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 techniques for shadow removal. Our method achieved scores of 0.196 (3rd out of 19) in LPIPS and 7.44 (3rd out of 19) in the …
abstract alignment analyze annotation arxiv challenge cs.cv detection discuss five function image improvements introduction key loss paper quality report shadow team technical type
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