首页|Unsupervised Multi-Expert Learning Model for Underwater Image Enhancement

Unsupervised Multi-Expert Learning Model for Underwater Image Enhancement

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原文链接
  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
Underwater image enhancement aims to restore a clean appearance and thus improves the quality of underwater degraded images.Current methods feed the whole image directly into the model for enhancement.However,they ignored that the R,G and B channels of underwater degraded images present varied degrees of degradation,due to the selective absorption for the light.To address this issue,we propose an unsupervised multi-expert learning model by considering the enhancement of each color channel.Specifically,an unsupervised architecture based on generative adversarial network is employed to alleviate the need for paired underwater images.Based on this,we design a genera-tor,including a multi-expert encoder,a feature fusion module and a feature fusion-guided decoder,to generate the clear underwater image.Accordingly,a multi-expert discriminator is proposed to verify the authenticity of the R,G and B channels,respectively.In addition,content perceptual loss and edge loss are introduced into the loss function to further improve the content and details of the enhanced images.Extensive experiments on public datasets demon-strate that our method achieves more pleasing results in vision quality.Various metrics(PSNR,SSIM,UIQM and UCIQE)eval-uated on our enhanced images have been improved obviously.

Multi-expert learningunderwater image enhance-mentunsupervised learning

Hongmin Liu、Qi Zhang、Yufan Hu、Hui Zeng、Bin Fan

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School of Intelligence Science and Technology and the Institute of Artificial Intelligence,University of Science and Technology Beijing,Beijing 100083,China

School of Automation and Electrical Engineering,University of Science and Technology Beijing,Beijing 100083,China

National Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaFundamental Research Funds for the Central UniversitiesPostgraduate Education Reform Project of Henan Province

2020YFB131300262276023U22B205562222302U2013202FRF-TP-22-003C12021SJGLX260Y

2024

10.1109/JAS.2023.123771

自动化学报(英文版)
中国自动化学会,中国科学院自动化研究所,中国科技出版传媒股份有限公司

自动化学报(英文版)

CSTPCDEI
ISSN：2329-9266
年,卷(期)：2024.11(3)
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