  Haze severely degrades the visual quality of remote sensing images and
hampers the performance of automotive navigation, intelligent monitoring, and
urban management. The emerging denoising diffusion probabilistic model (DDPM)
exhibits the significant potential for dense haze removal with its strong
generation ability. Since remote sensing images contain extensive small-scale
texture structures, it is important to effectively restore image details from
hazy images. However, current wisdom of DDPM fails to preserve image details
and color fidelity well, limiting its dehazing capacity for remote sensing
images. In this paper, we propose a novel unified Fourier-aware diffusion model
for remote sensing image dehazing, termed RSHazeDiff. From a new perspective,
RSHazeDiff explores the conditional DDPM to improve image quality in dense hazy
scenarios, and it makes three key contributions. First, RSHazeDiff refines the
training phase of diffusion process by performing noise estimation and
reconstruction constraints in a coarse-to-fine fashion. Thus, it remedies the
unpleasing results caused by the simple noise estimation constraint in DDPM.
Second, by taking the frequency information as important prior knowledge during
iterative sampling steps, RSHazeDiff can preserve more texture details and
color fidelity in dehazed images. Third, we design a global compensated
learning module to utilize the Fourier transform to capture the global
dependency features of input images, which can effectively mitigate the effects
of boundary artifacts when processing fixed-size patches. Experiments on both
synthetic and real-world benchmarks validate the favorable performance of
RSHazeDiff over multiple state-of-the-art methods. Source code will be released
at https://github.com/jm-xiong/RSHazeDiff.
