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Underwater single-image restoration based on modified generative adversarial net

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Abstract

In the water medium, different light attenuation rates will cause selective absorption, resulting in poor underwater image quality. The images have shortcomings such as color distortion and blurring, which affect people’s judgment. Therefore, to improve the underwater image quality, underwater single-image restoration based on modified generative adversarial net (GAN) is proposed. First, we introduce the ResNet component with small parameters into the generator to extract the deep features of the image. We adjust the order of the levels in the component and use the pre-activation method to improve the regularization ability. Secondly, the discriminator uses a local PatchGAN structure to improve image quality. To speed up the network convergence, a variety of loss functions such as GAN with Wasserstein distance and gradient penalty are introduced to jointly train the network. Finally, to improve the generalization ability of the model, the underwater images are synthesized based on the principle of the underwater dark channel prior and the underwater physical model. Experimental results show that the proposed method in this paper is better than several other underwater image restoration methods in terms of qualitative and quantitative performance.

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Acknowledgements

This study was supported by the National Key Research and Development Program of China (2017YFB0102500), the National Natural Science Foundation of China (61872158, 62172186), the Science and Technology Development Plan Project of Jilin Province (20190701019GH, 20200401132GX), the Korea Foundation for Advanced Studies’ International Scholar Exchange Fellowship for the academic year of 2017–2018, the Fundamental Research Funds for the Chongqing Research Institute Jilin University (2021DQ0009), and the Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Jindong Zhang, Dongyu Pan, Kunpeng Zhang, Jingyi Jin, Yuanzhi Ma & Mai Chen

  2. Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Jilin University, Changchun, 130012, China

    Jindong Zhang

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  1. Jindong Zhang
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  2. Dongyu Pan
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  3. Kunpeng Zhang
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Correspondence to Jindong Zhang.

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Zhang, J., Pan, D., Zhang, K. et al. Underwater single-image restoration based on modified generative adversarial net. SIViP 17, 1153–1160 (2023). https://doi.org/10.1007/s11760-022-02322-z

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