计算机与数字工程2024，Vol.52Issue(6)：1859-1863,5.DOI:10.3969/j.issn.1672-9722.2024.06.044
结合小波变换和注意力机制的U-NET图像去雾算法
U-NET Image Dehazing Algorithm Combining Wavelet Transform and Attention Mechanism
邱雨珉 1郭剑辉 1楼根铨 2张文俊2
作者信息
- 1. 南京理工大学计算机科学与工程学院 南京 210094
- 2. 江南造船(集团)有限责任公司 上海 201913
- 折叠
摘要
Abstract
Images are the basic usage data for many tasks,and have high requirements for their own quality.But the quality of the image is affected by many factors,such as fog in the air.Therefore,the study of image dehazing is very necessary.The emerging deep learning has played an important role in various computer vision tasks,as well as in single image dehazing.Based on the convo-lutional neural network,this paper proposes and designs a U-NET image dehazing model that combines wavelet transform and atten-tion mechanism.Wavelet transform replaces the up and down sampling in the original U-NET,and retains more detailed informa-tion.At the same time,the pixel attention mechanism and the channel attention mechanism are combined into an attention module,which is parallel to the U-NET module and exists as a feature supplement.In the visual effect and quantitative analysis,it is proved that the model has better dehazing effect.关键词
图像去雾/卷积神经网络/U-NET/小波变换/注意力机制Key words
image dehazing/convolutional neural network/U-NET/wavelet transform/attention mechanism分类
信息技术与安全科学引用本文复制引用
邱雨珉,郭剑辉,楼根铨,张文俊..结合小波变换和注意力机制的U-NET图像去雾算法[J].计算机与数字工程,2024,52(6):1859-1863,5.
