  Diffusion models have established new state of the art in a multitude of
computer vision tasks, including image restoration. Diffusion-based inverse
problem solvers generate reconstructions of exceptional visual quality from
heavily corrupted measurements. However, in what is widely known as the
perception-distortion trade-off, the price of perceptually appealing
reconstructions is often paid in declined distortion metrics, such as PSNR.
Distortion metrics measure faithfulness to the observation, a crucial
requirement in inverse problems. In this work, we propose a novel framework for
inverse problem solving, namely we assume that the observation comes from a
stochastic degradation process that gradually degrades and noises the original
clean image. We learn to reverse the degradation process in order to recover
the clean image. Our technique maintains consistency with the original
measurement throughout the reverse process, and allows for great flexibility in
trading off perceptual quality for improved distortion metrics and sampling
speedup via early-stopping. We demonstrate the efficiency of our method on
different high-resolution datasets and inverse problems, achieving great
improvements over other state-of-the-art diffusion-based methods with respect
to both perceptual and distortion metrics. Source code and pre-trained models
will be released soon.
