  Diffusion models have recently gained traction as a powerful class of deep
generative priors, excelling in a wide range of image restoration tasks due to
their exceptional ability to model data distributions. To solve image
restoration problems, many existing techniques achieve data consistency by
incorporating additional likelihood gradient steps into the reverse sampling
process of diffusion models. However, the additional gradient steps pose a
challenge for real-world practical applications as they incur a large
computational overhead, thereby increasing inference time. They also present
additional difficulties when using accelerated diffusion model samplers, as the
number of data consistency steps is limited by the number of reverse sampling
steps. In this work, we propose a novel diffusion-based image restoration
solver that addresses these issues by decoupling the reverse process from the
data consistency steps. Our method involves alternating between a
reconstruction phase to maintain data consistency and a refinement phase that
enforces the prior via diffusion purification. Our approach demonstrates
versatility, making it highly adaptable for efficient problem-solving in latent
space. Additionally, it reduces the necessity for numerous sampling steps
through the integration of consistency models. The efficacy of our approach is
validated through comprehensive experiments across various image restoration
tasks, including image denoising, deblurring, inpainting, and super-resolution.
