广西师范大学学报（自然科学版）2026，Vol.44Issue(2)：77-89,13.DOI:10.16088/j.issn.1001-6600.2025061001
多尺度非对称注意力遥感去雾Transformer
Multi-scale Asymmetric Attention Transformer for Remote Sensing Image Dehazing
摘要
Abstract
Haze interference can cause blurred structures and loss of details in remote sensing images,severely compromising the accuracy of downstream visual tasks.To address this challenge,this paper proposes a heterogeneous enhancement remote sensing image dehazing network that improves feature restoration from two perspectives:spatial structure modeling and frequency information integration.Specifically,a multi-sc ale asymmetric attention transformer module is designed,incorporating a direction-aware mechanism to enhance the modeling of blurred edges and texture details.In parallel,a wavelet-based adaptive high-low frequency enhancement module is constructed,utilizing Haar wavelet decomposition to separate frequency-domain information,where high-frequency and low-frequency submodules are employed to reinforce edge contours and structural representations,respectively.These two modules are embedded in the feature extraction and feature fusion stages,collaboratively addressing the limitations of traditional methods in directional modeling and high-frequency feature preservation.With low computational overhead,the proposed method achieves average PSNR/SSIM scores of 24.993 6/0.909 9 on the HAZE 1K dataset and 33.180 2/0.894 2 on the RICE dataset,demonstrating significant advantages in detail restoration.关键词
遥感图像去雾/Transformer/非对称注意力/高低频特征增强/小波变换/方向感知建模/深度学习Key words
remote sensing image dehazing/transformer/asymmetric attention/high-low frequency feature enhancement/wavelet transform/direction-aware modeling/deep learning分类
信息技术与安全科学引用本文复制引用
王旭阳,梁宇航..多尺度非对称注意力遥感去雾Transformer[J].广西师范大学学报（自然科学版）,2026,44(2):77-89,13.基金项目
国家自然科学基金(62161019) （62161019）
