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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.08995 |
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| _version_ | 1866917614440153088 |
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| author | Kondo, Yuki Miyata, Riku Yasue, Fuma Naruki, Taito Ukita, Norimichi |
| author_facet | Kondo, Yuki Miyata, Riku Yasue, Fuma Naruki, Taito Ukita, Norimichi |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_08995 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | NTIRE 2023 Image Shadow Removal Challenge Technical Report: Team IIM_TTI Kondo, Yuki Miyata, Riku Yasue, Fuma Naruki, Taito Ukita, Norimichi Computer Vision and Pattern Recognition 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). |
| title | NTIRE 2023 Image Shadow Removal Challenge Technical Report: Team IIM_TTI |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.08995 |