Taming Diffusion Models for Image Restoration: A Review

Understand

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.