A Survey on Diffusion Models for Inverse Problems
This survey is a valuable resource for researchers and practitioners interested in the intersection of diffusion models and inverse problems, offering a structured understanding of existing approaches.
This survey paper provides a comprehensive overview of methods that leverage pre-trained diffusion models as unsupervised priors to solve inverse problems, particularly in image restoration and reconstruction. It categorizes these methods based on problem types and techniques, analyzing their connections and practical implementations.
Diffusion models have become increasingly popular for generative modeling due to their ability to generate high-quality samples. This has unlocked exciting new possibilities for solving inverse problems, especially in image restoration and reconstruction, by treating diffusion models as unsupervised priors. This survey provides a comprehensive overview of methods that utilize pre-trained diffusion models to solve inverse problems without requiring further training. We introduce taxonomies to categorize these methods based on both the problems they address and the techniques they employ. We analyze the connections between different approaches, offering insights into their practical implementation and highlighting important considerations. We further discuss specific challenges and potential solutions associated with using latent diffusion models for inverse problems. This work aims to be a valuable resource for those interested in learning about the intersection of diffusion models and inverse problems.