  Blind face restoration usually synthesizes degraded low-quality data with a
pre-defined degradation model for training, while more complex cases could
happen in the real world. This gap between the assumed and actual degradation
hurts the restoration performance where artifacts are often observed in the
output. However, it is expensive and infeasible to include every type of
degradation to cover real-world cases in the training data. To tackle this
robustness issue, we propose Diffusion-based Robust Degradation Remover (DR2)
to first transform the degraded image to a coarse but degradation-invariant
prediction, then employ an enhancement module to restore the coarse prediction
to a high-quality image. By leveraging a well-performing denoising diffusion
probabilistic model, our DR2 diffuses input images to a noisy status where
various types of degradation give way to Gaussian noise, and then captures
semantic information through iterative denoising steps. As a result, DR2 is
robust against common degradation (e.g. blur, resize, noise and compression)
and compatible with different designs of enhancement modules. Experiments in
various settings show that our framework outperforms state-of-the-art methods
on heavily degraded synthetic and real-world datasets.

Computer Vision and Pattern Recognition