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 certain degradations,
and the latter aims at guiding the diffusion model to generate the
corresponding 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
advantage of the varying diffusion priors. Thanks to their degradation-agnostic
property, the all-in-one image restoration could be achieved in a zero-shot way
by ZeroAIR. 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.

ZeroAIR 是一种全能图像修复的方法，通过利用预先训练的扩散模型捕捉的图像先验信息，解决了图像降级建模和扩散引导的两个挑战，实现了零操作图像修复。