Dynamic-Routing 3D-Fusion Network for Remote Sensing Image Haze Removal

Recently, U-shaped neural networks (U-Net) and Full resolution convolutional neural networks (F-Net) have been extensively explored for remote sensing image haze removal, achieving excellent performance. However, downsampling in U-Net leads to significant loss of high-frequency information, while F-Net fails to satisfy the large receptive field demand of remote sensing images, resulting in suboptimal dehazing results for both architectures. Moreover, most existing haze removal methods neglect exploring the correlation between spatial and channel information in feature fusion, which is crucial for restoring image texture details and colors. To address these issues, we propose a Dynamic-Routing 3D-Fusion Network (DR3DF-Net), comprising a Dynamic Routing Features Framework (DRFF) and a 3D Perceptual Feature Fusion (3D-PFF) module. Specifically, the DRFF utilizes a Self-generated Constrained Feature Routing (SCFR) mechanism to learn the most representative features extracted from U-Net, F-Net, and their fused features to enhance clear image reconstruction. Furthermore, the 3D-PFF module enhances interaction between spatial and channel information of multiple features, assigning pixel-level weights for feature fusion, improving dehazed image texture details and colors. Experiments on challenging benchmark datasets demonstrate our DR3DF-Net outperforms several state-of-the-art haze removal methods. The source code is available at https://github.com/lslyttx/DR3DF-Net.