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.

翻译：扩散模型在生成建模领域取得了显著进展，尤其在提升图像质量以符合人类偏好方面。最近，这些模型也被应用于低级计算机视觉任务中，以实现照片级真实的图像复原，例如图像去噪、去模糊、去雾等。在本综述论文中，我们介绍了扩散模型的关键构建方法，并综述了利用扩散模型解决通用图像复原任务的当代技术。此外，我们指出了现有基于扩散的图像复原框架的主要挑战与局限性，并为未来工作提供了潜在方向。