Unpaired Underwater Image Enhancement Based on CycleGAN
Abstract
:1. Introduction
- We introduce a content loss regularizer into the generator in CycleGAN, which keeps more detailed information in the corresponding generated clear image. This strategy is different from CartoonGAN [24];
- We add a blur-promoting adversarial loss regularizer into the discriminator in CycleGAN, which reduces the effects of blur and noise and enhances the image clarity;
- We exploit the improved DenseNet Block in the generator to strengthen the forward transfer of feature maps, so that every feature map can be utilized;
- We test our proposed UW-CycleGAN on different types of underwater images and obtain a satisfactory performance.
2. Underwater Image Enhancement
3. Underwater CycleGAN (UW-CycleGAN)
- (1)
- The mapping function G generates the clear image from .
- (2)
- Another mapping function F reconstructs the degraded image x by .
- (3)
- Discriminator judges whether the generated image and clear image y derive from the same distribution.
3.1. Loss Function
3.1.1. Content Loss
3.1.2. Blur-Promoting Adversarial Loss
3.1.3. Full Loss Funtion
3.2. Network Architectures
4. Experiment and Evaluation
4.1. Datasets and Metrics
4.2. Experimental Assessment
4.3. Ablation Experiments
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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AG ↑ | IE ↓ | UIQM ↑ | |
---|---|---|---|
Deunderwater | 7.5047 | 7.8178 | 5.1460 |
HL | 7.3021 | 7.4033 | 4.0719 |
UCM | 4.9102 | 7.3955 | 3.8221 |
FUnIE-GAN-UP | 5.9444 | 7.3819 | 4.2130 |
CartoonGAN | 4.9079 | 7.2567 | 4.4997 |
CycleGAN | 6.4737 | 7.2785 | 4.8380 |
UW-CycleGAN | 7.6345 | 7.1824 | 5.1689 |
AG ↑ | IE ↓ | UIQM ↑ | |
---|---|---|---|
Deunderwater | 2.4945 | 7.7830 | 1.5500 |
HL | 2.0565 | 7.4271 | 1.5769 |
UCM | 2.5489 | 7.2451 | 2.0124 |
FUnIE-GAN-UP | 2.6014 | 7.3463 | 0.9782 |
CartoonGAN | 2.7224 | 6.7422 | 2.0883 |
CycleGAN | 2.9969 | 6.7452 | 2.6075 |
UW-CycleGAN | 3.1370 | 6.4827 | 2.7497 |
AG ↑ | IE ↓ | UIQM ↑ | |
---|---|---|---|
w/o | 7.3567 | 7.3727 | 5.1268 |
w/o | 7.5490 | 7.2750 | 5.1530 |
6.9984 | 7.2864 | 5.0271 | |
UW-CycleGAN | 7.6345 | 7.1824 | 5.1689 |
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Du, R.; Li, W.; Chen, S.; Li, C.; Zhang, Y. Unpaired Underwater Image Enhancement Based on CycleGAN. Information 2022, 13, 1. https://doi.org/10.3390/info13010001
Du R, Li W, Chen S, Li C, Zhang Y. Unpaired Underwater Image Enhancement Based on CycleGAN. Information. 2022; 13(1):1. https://doi.org/10.3390/info13010001
Chicago/Turabian StyleDu, Rong, Weiwei Li, Shudong Chen, Congying Li, and Yong Zhang. 2022. "Unpaired Underwater Image Enhancement Based on CycleGAN" Information 13, no. 1: 1. https://doi.org/10.3390/info13010001