  All-in-one aims to solve various tasks of image restoration in a single
model. To this end, we present a feasible way of exploiting the image priors
captured by the pretrained diffusion model, through addressing the two
challenges, i.e., degradation modeling and diffusion guidance. The former aims
to simulate the process of the clean image degenerated by the unknown
degradations, and the latter aims at guiding the diffusion model to generate
the desired clean image. With the motivations, we propose a zero-shot framework
for all-in-one image restoration, termed ZeroAIR, which alternatively performs
the test-time degradation modeling (TDM) and the three-stage diffusion guidance
(TDG) at each timestep of the reverse sampling. To be specific, TDM exploits
the diffusion priors to learn a degradation model from a given degraded image,
and TDG divides the timesteps into three stages for taking full advantages of
the varying diffusion priors. Thanks to their degradation-agnostic property,
all-in-one restoration could be achieved in a zero-shot way. Through extensive
experiments, we show that our ZeroAIR achieves comparable even better
performance than those task-specific methods. The code will be available on
Github.

Computer Vision and Pattern Recognition