Taming Diffusion Models for Image Restoration: A Review
It synthesizes existing research for practitioners in computer vision, but is incremental as it does not introduce novel methods.
This review paper surveys the application of diffusion models to image restoration tasks like denoising and deblurring, highlighting their progress and challenges without presenting new experimental results.
Diffusion models 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, dehazing, etc. In this review paper, we introduce key constructions in diffusion models and survey contemporary techniques that make use of diffusion models in solving general IR tasks. Furthermore, we point out the main challenges and limitations of existing diffusion-based IR frameworks and provide potential directions for future work.