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Physics-Aware Semi-Supervised Underwater Image Enhancement
April 30, 2024, 4:48 a.m. | Hao Qi, Xinghui Dong
cs.CV updates on arXiv.org arxiv.org
Abstract: Underwater images normally suffer from degradation due to the transmission medium of water bodies. Both traditional prior-based approaches and deep learning-based methods have been used to address this problem. However, the inflexible assumption of the former often impairs their effectiveness in handling diverse underwater scenes, while the generalization of the latter to unseen images is usually weakened by insufficient data. In this study, we leverage both the physics-based underwater Image Formation Model (IFM) and deep …
abstract arxiv cs.cv deep learning diverse however image images medium normally physics prior semi-supervised type underwater water while
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