水下无人系统学报2025，Vol.33Issue(6)：1065-1073,9.DOI:10.11993/j.issn.2096-3920.2025-0094
基于轻量化多尺度CNN的水下图像增强算法及边缘端部署
Lightweight Multi-Scale CNN-Based Underwater Image Enhancement Algorithm and Edge Deployment
张俊 1罗凡 2袁政1
作者信息
- 1. 中国电子科技集团公司第五十八研究所,江苏 无锡,214035
- 2. 武汉第二船舶设计研究所,湖北 武汉,430000
- 折叠
摘要
Abstract
This paper proposed a lightweight multi-scale convolutional neural network(CNN)-based underwater image enhancement algorithm to address the problems of noise interference,texture blur,color distortion,and high computational complexity and long time consumption of traditional enhancement algorithms caused by water scattering and absorption in underwater visible light images.The U-Net structure was used,which combined shallow texture features with deep semantic features to effectively restore the texture and color information of the image.A lightweight feature extraction module was introduced,which not only simplified the model parameters but also accelerated the convergence speed of the network.A multi-scale pyramid pooling was embedded in the backbone network for extracting multi-scale features and compensating for the shortcomings of traditional algorithms in detail restoration.By combining L1 loss with structural similarity index loss for joint optimization,the restoration effect of image brightness and contrast was improved.To meet the low latency requirements of engineering applications,the algorithm was quantized and then deployed on an embedded platform.By calling the embedded neural processing unit resources to accelerate the inference process,the forward inference on the Atlas200IA2 took only 28 ms.Through experiments on publicly available underwater datasets,the algorithm proposed in this paper achieved an underwater image quality measure of 4.33 and the underwater color image quality evaluation index of 0.63,respectively,on the test set,demonstrating the effectiveness of the proposed enhancement algorithm.关键词
水下图像增强/多尺度卷积神经网络/轻量化/U-Net结构/边缘端部署Key words
underwater image enhancement/multi-scale convolutional neural network/light weight/U-Net structure/edge deployment分类
军事科技引用本文复制引用
张俊,罗凡,袁政..基于轻量化多尺度CNN的水下图像增强算法及边缘端部署[J].水下无人系统学报,2025,33(6):1065-1073,9.
