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.

本研究解决了扩散模型在逆问题应用中的信息不足，提供了无监督先验的有效性综述。通过对现有方法的分类和比较，揭示了不同技术之间的联系，并讨论了使用潜在扩散模型面临的挑战与解决方案。这项工作为扩散模型与逆问题的结合提供了宝贵的参考资源。

关于扩散模型在逆问题中的应用研究