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.

翻译：雾霾严重降低了遥感图像的视觉质量，并阻碍了车载导航、智能监控和城市管理等应用的性能。新兴的去噪扩散概率模型（DDPM）凭借其强大的生成能力，在浓密雾霾去除方面展现出显著潜力。由于遥感图像包含大量小尺度纹理结构，有效恢复雾霾图像中的细节至关重要。然而，当前DDPM方法难以很好地保留图像细节和色彩保真度，限制了其在遥感图像去雾中的能力。本文提出一种新颖的统一傅里叶感知扩散模型用于遥感图像去雾，称为RSHazeDiff。从新视角出发，RSHazeDiff探索条件DDPM以提高浓雾场景下的图像质量，并包含三项关键贡献。首先，RSHazeDiff通过以由粗到细的方式执行噪声估计和重建约束来优化扩散过程的训练阶段，从而弥补DDPM中简单噪声估计约束导致的劣质结果。其次，在迭代采样步骤中将频率信息作为重要先验知识，使RSHazeDiff能够在去雾图像中保留更多纹理细节和色彩保真度。第三，我们设计了全局补偿学习模块，利用傅里叶变换捕获输入图像的全局依赖特征，有效减轻处理固定尺寸块时的边界伪影效应。在合成与真实场景基准上的实验验证了RSHazeDiff相较于多种最先进方法的优越性能。源代码将发布在https://github.com/jm-xiong/RSHazeDiff。