综述扩散模型在图像逆问题中的应用与挑战
Diffusion models for inverse problems
- 按显式逼近、变分推断等分类梳理方法
- 覆盖盲反问题、数据稀缺等复杂场景
- 适合关注生成模型在成像领域应用的研究者
近年来，利用扩散先验解决成像中的逆问题已日趋成熟。本章系统回顾了各类提出的方法，将其分为经典的显式近似方法与其他方法（包括变分推断、序列蒙特卡洛、解耦数据一致性）。涵盖更复杂的场景，如盲反问题、高维数据、数据稀缺与分布不匹配问题。还介绍了通过文本融合多模态信息的最新进展。本文旨在：(i) 提炼连接这些算法的共通数学脉络；(ii) 系统对比不同方法在代表性逆问题中的假设与性能权衡；(iii) 明晰扩散模型在逆问题求解中的理论与实践挑战，厘清该领域的研究图景。
原文摘要 · Abstract (English)
Using diffusion priors to solve inverse problems in imaging have significantly matured over the years. In this chapter, we review the various different approaches that were proposed over the years. We categorize the approaches into the more classic explicit approximation approaches and others, which include variational inference, sequential monte carlo, and decoupled data consistency. We cover the extension to more challenging situations, including blind cases, high-dimensional data, and problems under data scarcity and distribution mismatch. More recent approaches that aim to leverage multimodal information through texts are covered. Through this chapter, we aim to (i) distill the common mathematical threads that connect these algorithms, (ii) systematically contrast their assumptions and performance trade-offs across representative inverse problems, and (iii) spotlight the open theoretical and practical challenges by clarifying the landscape of diffusion model based inverse problem solvers.
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