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UUGAN: a GAN-based approach towards underwater image enhancement using non-pairwise supervision

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Abstract

Underwater image enhancement has become an emerging research field in recent years. Among various research studies, methods based on deep learning have gained a foothold and gradually expanded their influences. Most of these methods need pairs of training images, but due to the complexity of the underwater environment, it is challenging for us to obtain such expected datasets. Considering this problem, this paper explores an underwater image enhancement approach based on the unsupervised training mode. Concretely, a generative adversarial network (GAN) without pairwise image training is proposed, called UUGAN. It aims to bring the visual effects of expert images to the raw images. Our model has three parts, broadly speaking. Firstly, a GAN network based on an encoder-decoder is constructed; and secondly, a bridge connection scheme with intermediate layer feature transition is proposed. Thirdly, a loss function with multi-input constraints is applied. To demonstrate the effectiveness of UUGAN, we evaluate it on several real-world and synthetic datasets and compare it with some excellent methods. In the qualitative and quantitative comparison experiments, our methods have achieved remarkable results.

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Authors and Affiliations

  1. School of Marine Electrical Engineering, Dalian Maritime University, Dalian, China

    Huipu Xu, Xiangyang Long & Min Wang

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  1. Huipu Xu
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  2. Xiangyang Long
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  3. Min Wang
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Correspondence to Huipu Xu.

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Xu, H., Long, X. & Wang, M. UUGAN: a GAN-based approach towards underwater image enhancement using non-pairwise supervision. Int. J. Mach. Learn. & Cyber. 14, 725–738 (2023). https://doi.org/10.1007/s13042-022-01659-8

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  • DOI: https://doi.org/10.1007/s13042-022-01659-8

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