华中科技大学学报（自然科学版）2025，Vol.53Issue(3)：31-40,10.DOI:10.13245/j.hust.250377
基于全局特征提取和提示学习的水下图像增强
Underwater image enhancement based on global feature extraction and prompt learning
摘要
Abstract
To address the issues of low contrast,information loss,and color distortion in underwater imaging caused by light interference,water depth,and suspended particles,an improved CycleGAN-based underwater image enhancement algorithm was proposed based on global feature extraction and prompt learning.On the one hand,the global context module was used to extract the global information of the image in the encoder of the network,and the attention mechanism was introduced to pay attention to the important information part,so as to retain more detailed texture information in the process of downsampling.On the other hand,a multi-scale degradation prompt module was designed based on prompt learning technology in the decoder,which was injected layer by layer into the decoder to guide the network to perform better in denoising and deblurring while improving contrast,and the degradation information extracted from multi-scale features was encoded into easily learnable degradation prompts.In addition,the cascading loss function combining global similarity and perceived loss reduces the difference between the images in the low source domain and the target domain.In the experiment,three underwater image evaluation indexes were used:underwater image quality metric(UIQM),underwater color image quality metric(UCIQE)and natural image quality metric(NIQE).The results show that the enhanced images in this paper are 13.6%and 18.4%higher than the original CycleGAN in UIQM and UCIQE,respectively,and the combined values of the three indexes are better than those of other comparison algorithms,indicating that the proposed method improves the color quality and contrast of the images.At the same time,the details of the original image are also retained,and the effectiveness of the enhanced underwater image is finally proved by experiments.关键词
图像增强/生成对抗网络/注意力机制/提示学习/感知损失Key words
image enhancement/generative adversarial network/attention mechanism/prompt learning/perceptual loss分类
计算机与自动化引用本文复制引用
张明华,刘佳艺,石少华,宋巍..基于全局特征提取和提示学习的水下图像增强[J].华中科技大学学报（自然科学版）,2025,53(3):31-40,10.基金项目
国家自然科学基金资助项目(61972240). （61972240）
