Joint Conditional Diffusion Model for Image Restoration with Mixed
  Degradations
Yufeng Yue Meng Yu Luojie Yang Yi Yang
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.