Image restoration is rather challenging in adverse weather conditions, especially when multiple degradations occur simultaneously. Blind image decomposition was proposed to tackle this issue, however, its effectiveness heavily relies on the accurate estimation of each component. Although diffusion-based models exhibit strong generative abilities in image restoration tasks, they may generate irrelevant contents when the degraded images are severely corrupted. To address these issues, we leverage physical constraints to guide the whole restoration process, where a mixed degradation model based on atmosphere scattering model is constructed. Then we formulate our Joint Conditional Diffusion Model (JCDM) by incorporating the degraded image and degradation mask to provide precise guidance. To achieve better color and detail recovery results, we further integrate a refinement network to reconstruct the restored image, where Uncertainty Estimation Block (UEB) is employed to enhance the features. Extensive experiments performed on both multi-weather and weather-specific datasets demonstrate the superiority of our method over state-of-the-art competing methods.

翻译：图像恢复在恶劣天气条件下极具挑战性，尤其是当多种退化同时发生时。盲图像分解被提出以解决这一问题，然而其有效性高度依赖对每个分量的准确估计。尽管基于扩散的模型在图像恢复任务中展现出强大的生成能力，但当退化图像严重受损时，它们可能生成无关内容。为解决这些问题，我们利用物理约束引导整个恢复过程，构建了基于大气散射模型的混合退化模型。随后，我们通过引入退化图像和退化掩码来提供精确引导，提出了联合条件扩散模型（JCDM）。为实现更优的色彩与细节恢复效果，我们进一步集成精化网络来重建恢复图像，其中采用不确定性估计模块（UEB）增强特征。在多天气及特定天气数据集上进行的大量实验表明，我们的方法优于当前最先进的竞争方法。