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.

在恶劣天气条件下，图像恢复是一项具有挑战性的任务，而盲解析图像分解技术是为了解决这个问题而提出的一种方法，它依赖于每个部分的精确估计。为了解决这些问题，我们利用物理约束来指导整个恢复过程，构建基于大气散射模型的混合退化模型，并将退化图像和退化遮罩合并到我们的联合条件扩散模型中，以提供精确指导。为了实现更好的颜色和细节恢复结果，我们进一步整合了一个细化网络来重建恢复图像，其中采用了不确定性估计块来增强特征。在多种气象和特定天气数据集上进行的大量实验证明了我们方法在竞争方法中的优越性。