  Blind image restoration (IR) is a common yet challenging problem in computer
vision. Classical model-based methods and recent deep learning (DL)-based
methods represent two different methodologies for this problem, each with their
own merits and drawbacks. In this paper, we propose a novel blind image
restoration method, aiming to integrate both the advantages of them.
Specifically, we construct a general Bayesian generative model for the blind
IR, which explicitly depicts the degradation process. In this proposed model, a
pixel-wise non-i.i.d. Gaussian distribution is employed to fit the image noise.
It is with more flexibility than the simple i.i.d. Gaussian or Laplacian
distributions as adopted in most of conventional methods, so as to handle more
complicated noise types contained in the image degradation. To solve the model,
we design a variational inference algorithm where all the expected posteriori
distributions are parameterized as deep neural networks to increase their model
capability. Notably, such an inference algorithm induces a unified framework to
jointly deal with the tasks of degradation estimation and image restoration.
Further, the degradation information estimated in the former task is utilized
to guide the latter IR process. Experiments on two typical blind IR tasks,
namely image denoising and super-resolution, demonstrate that the proposed
method achieves superior performance over current state-of-the-arts.

Image and Video Processing;Computer Vision and Pattern Recognition