综述扩散模型在图像逆问题中的应用，无需额外训练即可修复与重建图像。
A Survey on Diffusion Models for Inverse Problems
- 将预训练扩散模型作为无监督先验，直接用于图像修复和重建。
- 系统分类现有方法，梳理不同技术路径间的关联与差异。
- 聚焦潜在空间扩散模型的挑战与解决方案，适合算法研究者参考。
扩散模型因其生成高质量样本的能力，在生成建模中日益流行。这为解决逆问题（尤其是图像修复与重建）开辟了新途径，通过将扩散模型视为无监督先验。本综述全面回顾了利用预训练扩散模型求解逆问题而无需额外训练的方法。我们提出分类体系，按问题类型与技术手段对方法进行归类。分析不同方法间的联系，提供实际实现的洞察并指出关键注意事项。进一步讨论使用潜在扩散模型解决逆问题时面临的特定挑战及可能的应对策略。本工作旨在成为关注扩散模型与逆问题交叉领域的研究人员的重要参考资料。
原文摘要 · Abstract (English)
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.
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