Open AccessUCT‐GAN: underwater image colour transfer generative adversarial network
Author(s) -
Deng Junjie,
Luo Gege,
Zhao Caidan
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2020.0003
Subject(s) - underwater , computer science , artificial intelligence , image (mathematics) , computer vision , visibility , block (permutation group theory) , function (biology) , generative adversarial network , image quality , pattern recognition (psychology) , mathematics , geography , meteorology , geometry , archaeology , evolutionary biology , biology
Underwater image enhancement algorithms improve image quality and indirectly enhance underwater visibility. Although many underwater image enhancement neural networks have been proposed, they require large amounts of data. To reduce the amount of data required while providing better image enhancement, this study proposes an underwater image colour transfer generative adversarial network (UCT‐GAN). The authors first design a non‐linear mapping function to generate colour cast images according to original images. Then, the authors utilise these image pairs (i.e. colour cast images and corresponding original images) to guide the UCT‐GAN in learning the inverse function of the designed non‐linear mapping function. Finally, colour cast images are restored via the inverse function. A data augmentation method based on Poisson fusion and block combination is also proposed to overcome the problem of requiring a large amount of training data. Moreover, the authors extend UCT‐GAN into a multi‐class colour transfer network to achieve an array of underwater image enhancements. Experimental results indicate that the proposed UCT‐GAN can more effectively resolve underwater image colour cast compared to existing algorithms.