WaveDiff-R: Wavelet-Guided Diffusion Network with Residual Sub-Band Enhancement for Remote Sensing Dehazing
Miao Zhang, Shiqun Yin · Atmosphere · 2026
Atmospheric haze is a major source of image degradation in Earth observation systems, reducing visibility, distorting spectral information, and obscuring surface details in remote sensing imagery. Physics-based dehazing methods often hinge on simplified atmospheric assumptions, whereas purely data-driven networks struggle with ultra-high-resolution overhead imagery and the wide spatial variability of haze. To address these challenges in a way that respects the characteristics of very large remote sensing scenes, we introduce WaveDiff-R, a wavelet-guided diffusion framework with residual sub-band enhancement. Rather than running diffusion directly in the full spatial domain, WaveDiff-R performs a multi-level discrete wavelet transform (DWT) to separate low- and high-frequency components in a geometry-aware manner. The wavelet-guided diffusion module (WGDM) performs conditional diffusion only on the low-frequency approximation coefficients AK after a K-level DWT, reducing the denoising target by 4K while restoring global luminance and chromaticity. In parallel, the residual sub-band enhancement module (RSEM), built with residual state space blocks (RSSBs), refines the high-frequency sub-bands, recovering sharp edges and textures by jointly modeling long-range dependencies and local details. This collaborative design couples global consistency with fine-grained fidelity while maintaining an efficiency suitable for real-world remote sensing pipelines. Extensive experiments on six benchmark datasets covering synthetic and real scenarios showed that WaveDiff-R achieved consistently strong results, surpassing state-of-the-art natural-image and remote sensing dehazing baselines in both quantitative metrics and visual quality.