Taming diffusion models for image restoration: a review

Diffusion models (DMs) have achieved remarkable progress in generative modelling, particularly in enhancing image quality to conform to human preferences. Recently, these models have also been applied to low-level computer vision for photo-realistic image restoration (IR) in tasks such as image denoising, deblurring and dehazing. In this review, we introduce key constructions in DMs and survey contemporary techniques that make use of DMs in solving general IR tasks. We also point out the main challenges and limitations of existing diffusion-based IR frameworks and provide potential directions for future work.This article is part of the theme issue 'Generative modelling meets Bayesian inference: a new paradigm for inverse problems'.