Bidirectional-Modulation Frequency-Heterogeneous Network for Remote Sensing Image Dehazing

Hang Sun, Qingfei Zhong, Bo Du, Zhigang Tu, Jun Wan, Wenbin Wang, Dong Ren

Abstract: Recently, deep neural networks have been extensively explored in remote sensing image haze removal and achieved remarkable performance. However, existing methods fail to effectively fuse the features extracted from Convolutional Neural Networks (CNNs) and Transformer networks, leading to performance degradation. Moreover, most dehazing methods lack further exploration of the distinct properties of high- and low-frequency features, which are crucial for texture restoration and haze removal. To address these issues, we propose a Bidirectional-Modulation Frequency-Heterogeneous Network (BMFH-Net). Specifically, we propose a Differential-Expert Guided Bidirectional Modulation (DGBM) module that incorporates Differential experts and physical inversion models to exploit the complementarity of CNN-Transformer features and extract their latent haze-related physical characteristics, thereby enabling more effective bidirectional alignment. Furthermore, a Wavelet Frequency Heterogeneous Enhancement (WFHE) Module is designed to capture the most representative high-frequency features to refine image texture details, while enhancing the global perception of haze and reconstructing structural information during low-frequency processing. Experiments on challenging remote sensing image datasets demonstrate that our BMFH-Net outperforms several state-of-the-art haze removal methods. The code is released publicly at https://github.com/zqf2024/BMFH-Net

External IDs:doi:10.1109/tcsvt.2025.3570998