Adaptive Weighted Multi-Discriminator CycleGAN for Underwater Image Enhancement
AbstractIn this paper, we propose a novel underwater image enhancement method. Typical deep learning models for underwater image enhancement are trained by paired synthetic dataset. Therefore, these models are mostly effective for synthetic image enhancement but less so for real-world images. In contrast, cycle-consistent generative adversarial networks (CycleGAN) can be trained with unpaired dataset. However, performance of the CycleGAN is highly dependent upon the dataset, thus it may generate unrealistic images with less content information than original images. A novel solution we propose here is by starting with a CycleGAN, we add a pair of discriminators to preserve contents of input image while enhancing the image. As a part of the solution, we introduce an adaptive weighting method for limiting losses of the two types of discriminators to balance their influence and stabilize the training procedure. Extensive experiments demonstrate that the proposed method significantly outperforms the state-of-the-art methods on real-world underwater images. View Full-Text
Share & Cite This Article
Park, J.; Han, D.K.; Ko, H. Adaptive Weighted Multi-Discriminator CycleGAN for Underwater Image Enhancement. J. Mar. Sci. Eng. 2019, 7, 200.
Park J, Han DK, Ko H. Adaptive Weighted Multi-Discriminator CycleGAN for Underwater Image Enhancement. Journal of Marine Science and Engineering. 2019; 7(7):200.Chicago/Turabian Style
Park, Jaihyun; Han, David K.; Ko, Hanseok. 2019. "Adaptive Weighted Multi-Discriminator CycleGAN for Underwater Image Enhancement." J. Mar. Sci. Eng. 7, no. 7: 200.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.