A multi-scale convolutional neural network based underwater image enhancement algorithm and edge deployment

ZHANG Jun; LUO Fan; YUAN Zheng

doi:10.11993/j.issn.2096-3920.2025-0094

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ZHANG Jun, LUO Fan, YUAN Zheng. A multi-scale convolutional neural network based underwater image enhancement algorithm and edge deployment[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2025-0094

Citation:

ZHANG Jun, LUO Fan, YUAN Zheng. A multi-scale convolutional neural network based underwater image enhancement algorithm and edge deployment[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2025-0094

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A multi-scale convolutional neural network based underwater image enhancement algorithm and edge deployment

doi: 10.11993/j.issn.2096-3920.2025-0094

1.
The 58th Research Institute of China Electronics Technology Group Corporation, Wuxi 214035, China
2.
Wuhan Second Ship Design and Research Institute, Wuhan 430000, China
3.
The 58th Research Institute of China Electronics Technology Group Corporation, Wuxi 214035, China

Received Date: 2025-07-25
Accepted Date: 2025-09-01
Rev Recd Date: 2025-08-27

Available Online: 2025-11-24

Abstract

Abstract

This paper proposes a multi-scale convolutional neural network-based underwater image enhancement algorithm to address the problems of noise interference, texture blur, color distortion, and high computational complexity and time consumption of traditional enhancement algorithms caused by water scattering and absorption in underwater visible light images. Firstly, the entire network is designed using the U-Net structure, which combines shallow texture features with deep semantic features to effectively restore the texture and color information of the image. Secondly, in order to reduce the model parameters, a lightweight feature extraction module can be introduced, which can reduce the model parameters and accelerate the convergence of the network. Introducing multi-scale pyramid pooling in the backbone network for extracting multi-scale features compensates for the shortcomings of traditional algorithms in detail restoration. Finally, by combining L1 loss with structural similarity index(SSIM) loss, the network can effectively improve the restoration of image brightness and contrast. In order to reduce the time required for forward inference of the algorithm, the algorithm proposed in this paper was quantified and deployed on an embedded platform. By calling neural processing unit(NPU) resources to accelerate network model inference, the forward inference time on Atlas 200I A2 was only 28ms, meeting the low latency requirements for engineering applications. Through experiments on publicly available underwater datasets, the multi-scale convolutional neural network algorithm proposed in this paper achieved underwater image quality measure(UIQM) and uncertainty in color, intensity, and saturation of an image(UCIQE) of 4.33 and 0.63, respectively, on the test set, demonstrating the effectiveness of the proposed enhancement algorithm.
- underwater image enhancement,
- U-Net,
- lightweight,
- algorithm deployment

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References(24)

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