Journal of Atmospheric and Environmental Optics, Volume. 19, Issue 3, 381(2024)
Lightweight underwater image enhancement network based on cross-scale deep distillation feature perception
Fig. 1. Lightweight underwater image enhancement network structure diagram based on CSDDFP module
Fig. 2. Comparisons of parameter size and objective performance on LSUI400 dataset
Fig. 3. Visual comparisons of the mainstream methods on four benchmark datasets. (a) Input image; (b) UGAN; (c) WaterNet; (d) FUnIE_GAN; (e) UT-UIE; (f) ours; (g) ground truth
Fig. 4. Visual comparisons of the mainstream methods under insufficient illumination in deep water. (a) Input image; (b) UGAN; (c) WaterNet; (d) FUnIE_GAN; (e) UT-UIE; (f) ours; (g) ground truth
Fig. 5. Visual comparisons of the mainstream methods on texture details. (a) Input image; (b) UGAN; (c) WaterNet; (d) FUnIE_GAN; (e) UT-UIE; (f) ours; (g) ground truth
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Xiaohua WU, Zenglu LI, Zhanghua XU, Jingchun ZHOU. Lightweight underwater image enhancement network based on cross-scale deep distillation feature perception[J]. Journal of Atmospheric and Environmental Optics, 2024, 19(3): 381
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Received: Oct. 31, 2023
Accepted: --
Published Online: Jul. 17, 2024
The Author Email: Jingchun ZHOU (zhoujingchun03@qq.com)