Diffusion models represent a novel type of generative artificial intelligence models. Compared to traditional networks such as generative adversative networks, variational autoencoders, and flow models, diffusion models are characterized by their robust training, high fidelity and diversity in generation, and strong mathematical interpretability, and so they are widely used in fields of computer vision, signal processing, multi-modal learning and so on. Diffusion models are capable of sufficiently learning and exploring the deep generative priors from the training images, providing a novel paradigm for solving inverse problems in image processing. In order to systematically sort out the development status of diffusion model, especially the latest progress in solving the inverse problem of image processing, the research of diffusion model for the inverse problem of image processing was reviewed. The basic principle and development status of diffusion model was expounded,the main technical route of using diffusion model to solve the inverse problem of image processing and some specific application results in this direction were emphatically introduced, and the future research directions were envisioned.