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Diffusion models have established new state of the art in a multitude ofcomputer vision tasks, including image restoration. Diffusion-based inverseproblem solvers generate reconstructions of exceptional visual quality fromheavily corrupted measurements. However, in what is widely known as theperception-distortion trade-off, the price of perceptually appealingreconstructions is often paid in declined distortion metrics, such as PSNR.Distortion metrics measure faithfulness to the observation, a crucialrequirement in inverse problems. In this work, we propose a novel framework forinverse problem solving, namely we assume that the observation comes from astochastic degradation process that gradually degrades and noises the originalclean image. We learn to reverse the degradation process in order to recoverthe clean image. Our technique maintains consistency with the originalmeasurement throughout the reverse process, and allows for great flexibility intrading off perceptual quality for improved distortion metrics and samplingspeedup via early-stopping. We demonstrate the efficiency of our method ondifferent high-resolution datasets and inverse problems, achieving greatimprovements over other state-of-the-art diffusion-based methods with respectto both perceptual and distortion metrics. Source code and pre-trained modelswill be released soon.

DiracDiffusion: Denoising and Incremental Reconstruction with Assured Data-Consistency