Unpaired Underwater Image Enhancement Based on CycleGAN

Information

Yong Zhang¹  Congying Li²  Weiwei Li³  Shudong Chen³  Rong Du³ 

[1] Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;Information Research Center of Military Science, Beijing 100142, China;Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China;

关键词: image enhancement;    CycleGAN;    underwater image;    unpaired data;   

DOI  :  10.3390/info13010001

来源: DOAJ

【 摘 要 】

Underwater image enhancement recovers degraded underwater images to produce corresponding clear images. Image enhancement methods based on deep learning usually use paired data to train the model, while such paired data, e.g., the degraded images and the corresponding clear images, are difficult to capture simultaneously in the underwater environment. In addition, how to retain the detailed information well in the enhanced image is another critical problem. To solve such issues, we propose a novel unpaired underwater image enhancement method via a cycle generative adversarial network (UW-CycleGAN) to recover the degraded underwater images. Our proposed UW-CycleGAN model includes three main modules: (1) A content loss regularizer is adopted into the generator in CycleGAN, which constrains the detailed information existing in one degraded image to remain in the corresponding generated clear image; (2) A blur-promoting adversarial loss regularizer is introduced into the discriminator to reduce the blur and noise in the generated clear images; (3) We add the DenseNet block to the generator to retain more information of each feature map in the training stage. Finally, experimental results on two unpaired underwater image datasets produced satisfactory performance compared to the state-of-the-art image enhancement methods, which proves the effectiveness of the proposed model.

【 授权许可】

Unknown