基于视觉多头注意力与跨层白化的水下图像增强网络

doi:10.16451/j.cnki.issn1003-6059.202305002

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Abstract Due to the phenomena of light absorption and scattering, as well as the presence of small particles in underwater environment, underwater images suffer from the problems of color imbalance and detail distortion. To address this issue, an underwater image enhancement network based on visual multi-head attention and skip-layer whitening is proposed in this paper. A hierarchical architecture is adopted, feature extraction is performed by the encoding path and image reconstruction is carried out by the decoding path. The main components of the encoding and decoding paths are visual multi-head self-attention blocks. Instance whitening is applied to shallow features. The features of shallow layer after instance whitening are embedded into the features of deep layer by skip-layer connection as skip-layer whitening path. Content loss and structure loss are employed in the training process of the proposed network. The comparative experiment on benchmark underwater image datasets demonstrates the effectiveness of visual multi-head self-attention and instance whitening for underwater enhancement task, both quantitatively and visually.

Key words： Underwater Imaging Visual Attention Skip-Layer Whitening Quality Restoration

Received: 16 March 2023

ZTFLH:

TP391

Fund:National Natural Science Foundation of China(No.62172090), Start-up Research Fund of Southeast University(No.RF1028623097)

Corresponding Authors: ZHANG Jun, Ph.D., professor. His research interests include computer vision and pattern recognition.

About author:: CONG Xiaofeng, Ph.D. candidate. His research interests include underwater image enhancement and image dehazing.GUI Jie, Ph.D., professor. His research interests include generative algorithm, image dehazing and self-supervised learning.HE Lei, Ph.D., associate professor. His research interests include cyberspace security and endogenous safety.

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Cite this article:

CONG Xiaofeng,GUI Jie,HE Lei等. Underwater Image Enhancement Network Based on Visual Multi-head Attention and Skip-Layer Whitening[J]. Pattern Recognition and Artificial Intelligence, 2023, 36(5): 407-418.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202305002 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2023/V36/I5/407

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