Turbid Water Image Enhancement Algorithm Based on Improved CycleGAN
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摘要: 针对循环生成对抗网络(Cycle Generative Adversarial Networks, CycleGAN)在浑浊水体图像增强中存在质量差和速度慢的问题，该文提出一种可扩展、可选择和轻量化的特征提取单元BSDK (Bottleneck Selective Dilated Kernel)，并利用BSDK设计了一个新的生成器网络BSDKNet。与此同时，提出一种多尺度损失函数MLF(Multi-scale Loss Function)。在自建的浑浊水体图像增强数据集TC(Turbid and Clear)上，该文BM-CycleGAN比原始CycleGAN的精度提升3.27%，生成器网络参数降低4.15MB，运算时间减少0.107s。实验结果表明BM-CycleGAN适合浑浊水体图像增强任务。Abstract: In order to solve the problem of poor quality and slow speed in turbid water image enhancement based on Cycle Generative Adversarial Networks (CycleGAN), a scalable, selective and efficient block Bottleneck Selective Dilated Kernel (BSDK) is proposed, and a new generator network BSDKNet is redesigned by stacking BSDK. At the same time, Multi-scale Loss Function (MLF) is proposed to improve the structural similarity of the clear water image and the generated clear water image. On our turbid water image enhancement dataset Turbid and Clear (TC), the classification accuracy of the proposed BM-CycleGAN is 3.27% higher than that of classical CycleGAN. The generator parameters of BM-CycleGAN is 4.15 MB lower than that of CycleGAN, and the time consuming of BM-CycleGAN is 0.107 s less than that of CycleGAN. The experimental results show that BM-CycleGAN is suitable for turbid water image enhancement.
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表 1 浑浊水体图像增强算法性能比较
分类准确率(%) 参数大小(MB) 运算时间(s) CycleGAN 95.10 10.97 0.127 SS-CycleGAN 95.92 10.97 0.127 BSK-CycleGAN 96.73 18.21 0.054 BM-CycleGAN 98.37 6.82 0.020 
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表 2 BSDKNet生成器网络和MLF损失函数对BM-CycleGAN性能的影响
BSDKNet MLF 分类准确率(%) 参数大小(MB) 运算时间(s) CycleGAN 95.10 10.97 0.127 M-CycleGAN
B-CycleGAN
BM-CycleGAN√ 96.09 10.97 0.127 √ 97.27 6.82 0.020 √ √ 98.37 6.82 0.020
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表 3 不同权重系数(1–9)MLF对BM-CycleGAN性能的影响
1 2 3 4 5 6 7 8 9 分类准确率(%) 95.10 95.51 96.73 97.14 97.55 98.37 97.96 97.14 96.33
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表 4 不同多尺度系数(1,2,4,8)条件下的BSDKNet对BM-CycleGAN性能的影响
1 2 4 8 分类准确率(%) 95.10 97.96 98.37 98.78 参数大小(MB) 6.82 6.82 6.82 6.82 运算时间(s) 0.006 0.016 0.021 0.030
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