渔业现代化2024，Vol.51Issue(6)：115-124,10.DOI:10.3969/j.issn.1007-9580.2024.06.012
基于多尺度残差连接的水下图像自适应增强
An adaptive enhancement method for underwater images based on multi-scale residual connection
摘要
Abstract
To address common issues in underwater images,such as color distortion and reduced contrast,as well as the limitations of supervised methods that rely on large-scale paired high-quality underwater image datasets,an unsupervised underwater image enhancement method is proposed.This method utilizes a conditional variational autoencoder(cVAE)combined with probabilistic adaptive instance normalization(PAdaIN)and multi-color space stretching techniques to improve the visual quality of generated images while ensuring consistency with the original input images.Furthermore,a multi-scale residual connection module is employed to effectively reduce the transmission of non-essential information,thereby enhancing the model's performance.This approach provides an alternative to traditional training methods that rely on reference images.Experimental results demonstrate that this method achieves a 12%and 3%improvement in Peak Signal-to-Noise Ratio(PSNR)and Structural Similarity Index(SSIM)on the test set compared to FunieGAN and Water-Net,respectively,significantly enhancing the visual quality of the improved images.Moreover,the method performs excellently across different test sets,demonstrating its robust generalization capability.The study indicates that,without the need for reference images,this approach significantly improves underwater image quality,effectively enhancing image detail and color correction,and provides a viable solution for aquaculture and marine monitoring applications.关键词
水下图像增强/无监督学习/多尺度残差连接/图像处理/概率模型Key words
underwater image enhancement/unsupervised learning/multi-scale residual connections/image processing/probabilistic models分类
信息技术与安全科学引用本文复制引用
谢小文,袁红春..基于多尺度残差连接的水下图像自适应增强[J].渔业现代化,2024,51(6):115-124,10.基金项目
国家自然科学基金项目"基于海洋大数据深度学习的渔情预测模型研究(41776142)" （41776142）
