计算机工程与应用2025，Vol.61Issue(4)：241-252,12.DOI:10.3778/j.issn.1002-8331.2309-0455
两阶段特征迁移图像去雾算法
Two-Stage Feature Transfer Image Dehazing Algorithm
摘要
Abstract
To solve the problems such as artifacts,color distortion and unsatisfactory dehazing effect on images under the influence of non-uniform fog after image processing by common dehazing algorithms,a two-stage feature transfer image dehazing algorithm is proposed,which is implemented based on the encoder-decoder structure.In the first stage,the clear image is sent to the feature learning network,and the spatial structure information and color rules of the clear image are learned through the hybrid attention mechanism.In the second stage,the feature transfer loss is used to transfer the clear image feature knowledge learned in the feature learning network to the feature refinement image dehazing network.At the same time,the image context information is effectively extracted and fused through the multi-scale feature extraction module and the global feature refinement block.Finally,the output of the two stages is fused to restore a clear and dehazing image.The experimental results show that the algorithm has a good dehazing effect in the RESIDE dataset and real non-uniform foggy images,and the color of the processed image is reasonable and more in line with human visual perception.关键词
图像去雾/卷积神经网络/特征迁移/特征学习/混合注意力机制/全局特征细化Key words
image dehazing/convolutional neural network/feature transfer/feature learning/hybrid attention mecha-nism/global feature refinement分类
信息技术与安全科学引用本文复制引用
袁姮,颜廷昊,张晟翀..两阶段特征迁移图像去雾算法[J].计算机工程与应用,2025,61(4):241-252,12.基金项目
国家自然科学基金(61172144) （61172144）
辽宁省自然科学基金(20170540426) （20170540426）
辽宁省教育厅重点基金(LJYL049). （LJYL049）
