NTIRE 2023 Image Shadow Removal Challenge Technical Report: Team IIM_TTI
Published in
arXiv.org(2024)External Links:
TL
TL;DR
Five key improvements are implemented: 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.
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).
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Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded Images
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ShadowFormer: Global Context Helps Image Shadow Removal
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Joint Learning of Object Detection and Pose Estimation using Augmented Autoencoder
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Group Activity Recognition Using Joint Learning of Individual Action Recognition and People Grouping
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