摘要
雾霾干扰会导致遥感图像结构模糊、细节丢失,严重影响下游视觉任务的准确性。为此,本文提出一种异构增强的遥感图像去雾网络,从空间结构建模与频率信息整合2个层面提升特征恢复能力。具体而言,设计多尺度非对称注意力Transformer模块,引入方向感知机制以增强模糊边缘与纹理细节的建模;同时构建基于小波变换高低频自适应增强模块,使用Haar小波分解分离频域信息,分别通过高频与低频子模块强化边缘轮廓与结构表达。2个模块分别嵌入特征提取与融合阶段,协同缓解传统方法方向性建模不足与高频特征易丢失等问题。在保持低计算开销的前提下,本文方法在HAZE1K与RICE数据集上的平均PSNR/SSIM性能分别达到24.9936/0.9099与33.1802/0.8942,在细节恢复方面表现出显著优势。
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-scale 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 24.9936/0.9099 on the HAZE1K dataset and 33.1802/0.8942 on the RICE dataset,demonstrating significant advantages in detail restoration.
作者
王旭阳
梁宇航
WANG Xuyang;LIANG Yuhang(School of Computer and Communication,Lanzhou University of Technology,Lanzhou Gansu 730050,China)
出处
《广西师范大学学报(自然科学版)》
北大核心
2026年第2期77-89,共13页
Journal of Guangxi Normal University:Natural Science Edition
基金
国家自然科学基金(62161019)。
