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Adaptive Weighted Multi-Discriminator CycleGAN for Underwater Image Enhancement

1
School of Electrical Engineering, Korea University, Seoul 02841, Korea
2
Information Science Division, ARL, Adelphi, MD 20783, USA
*
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2019, 7(7), 200; https://doi.org/10.3390/jmse7070200
Received: 28 May 2019 / Revised: 26 June 2019 / Accepted: 26 June 2019 / Published: 28 June 2019
(This article belongs to the Special Issue Underwater Imaging)
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Abstract

In 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
Keywords: underwater; image enhancement; generative adversarial networks underwater; image enhancement; generative adversarial networks
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Park, J.; Han, D.K.; Ko, H. Adaptive Weighted Multi-Discriminator CycleGAN for Underwater Image Enhancement. J. Mar. Sci. Eng. 2019, 7, 200.

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