光学精密工程2024，Vol.32Issue(10)：1582-1594,13.DOI:10.37188/OPE.20243210.1582
综合多尺度信息和注意力机制的水下图像增强
Underwater image enhancement synthesizing multi-scale information and attention mechanisms
摘要
Abstract
Aiming at the problems of color distortion and detail loss in underwater images due to water scattering and absorption,a generative adversarial network model integrating multi-scale information and attention mechanism was proposed to enhance underwater images.Firstly,to fully exploit and enhance both local and global information of the image,local encoders and global encoders were employed to ex-tract local and global features respectively,which were then fused to achieve complementarity.Next,a multi-scale hybrid convolution was designed to capture multi-scale information,increasing the network's adaptability to features at different scales.Subsequently,attention mechanisms were utilized to enhance the accuracy of feature extraction,emphasizing the focus on high-value features.Finally,by iteratively ap-plying multi-scale hybrid convolution and attention mechanisms to refine features,the enhanced image was gradually up-sampled.Compared with the six classical and state-of-the-art methods,the proposed model not only achieved the best visual perception in subjective evaluations but also outperformed the six compar-ative methods on the entire test set in terms of four objective evaluation metrics peak signal-to-noise ratio(PSNR),structural similarity(SSIM),underwater image quality measurement(UIQM),and natural im-age quality evaluation(NIQE)with average scores of 22.499,0.789,2.911,and 4.175,respectively.The improvements over the best scores among the comparative methods are 0.353,0.002,0.025,and 0.307,respectively.These results indicate that the proposed model not only corrects image color distor-tion but also performs well in restoring image details,increasing image contrast,and enhancing clarity.Therefore,it shows promising prospects for practical applications in underwater image enhancement.关键词
水下图像增强/生成对抗网络/编码器/多尺度混合卷积/注意力机制Key words
underwater image enhancement/generative adversarial network/encoder/multi-scale hy-brid convolution/attention mechanism分类
信息技术与安全科学引用本文复制引用
夏晓华,钟预全,胡鹏,姚运仕,耿继光,张良奇..综合多尺度信息和注意力机制的水下图像增强[J].光学精密工程,2024,32(10):1582-1594,13.基金项目
国家自然科学基金(No.61901056) （No.61901056）
秦创原引用高层次创新创业人才项目(No.QCYRCXM-2022-352) （No.QCYRCXM-2022-352）
