计算机与现代化Issue(3)：78-85,92,9.DOI:10.3969/j.issn.1006-2475.2025.03.012
内容引导注意力融合的多尺度特征图像去雾算法
Multi-scale Feature Image Defogging Algorithm Based on Content-guided Attention Fusion
摘要
Abstract
Aiming at the problems of color distortion and detail blur in current defogging methods,a multi-scale feature image defogging algorithm based on content-guided attention fusion is proposed with encoder-decoder network architecture.Firstly,multi-scale feature extraction module is used to encode,and three parallel expanded convolutions with different scales and squeeze and excitation attention are designed to enlarge the receptor field,extract features of different scales,and improve fea-ture utilization.Secondly,in the decoder,the content-guided attention fusion module is designed to dynamically improve differ-ent weights for the deep and the shallow features to retain more effective feature information.Finally,pyramid scene parsing net-work is introduced to improve the ability of global information acquisition.The experimental results show that compared with other algorithms,the proposed algorithm improves 26.13%and 6.39%on the peak signal-to-noise ratio and structural similarity of SOTS datasets,respectively.The entropy and average gradient of the real fog datasets are increased by 3.27%and 21.09%re-spectively.The proposed algorithm improves the problem of defog incompleteness and detail blur.关键词
图像去雾/特征融合/注意力机制/多尺度特征/深度学习Key words
image defogging/feature fusion/attention mechanism/multi-scale features/deep learning分类
信息技术与安全科学引用本文复制引用
蒲亚亚,王彦博,苏勇东,徐忠承..内容引导注意力融合的多尺度特征图像去雾算法[J].计算机与现代化,2025,(3):78-85,92,9.基金项目
陕西省自然科学基金面上项目(2022JM-056) （2022JM-056）
