提出MCLC模块，用物理一致的梯度修正稳定扩散模型逆问题求解。
Measurement-Consistent Langevin Corrector for Stabilizing Latent Diffusion Inverse Problem Solvers
- 基于测量一致性设计朗之万更新，修正扩散模型求解器动态
- 在隐空间中实现更稳定可靠的结果，避免传统方法的发散问题
- 无需线性假设，适合复杂隐空间结构，适用于医学图像重建等场景
尽管潜变量扩散模型（LDM）已成为逆问题的强大先验，但现有的基于LDM的求解器常出现不稳定性。本文首次将不稳定性归因于求解器动力学与扩散模型学习到的稳定反向扩散过程之间的差异，并证明缩小这一差距可显著提升稳定性。基于此，我们提出测量一致朗之万校正器（MCLC），一种理论完备、可即插即用的稳定化模块，通过测量一致的朗之万更新修复基于LDM的逆问题求解器。相比依赖线性流形假设的先前方法（这些假设在隐空间中常不成立），MCLC提供了一个原则性的稳定机制，在隐空间中实现更稳定可靠的性能表现。
原文摘要 · Abstract (English)
While latent diffusion models (LDMs) have emerged as powerful priors for inverse problems, existing LDM-based solvers frequently suffer from instability. In this work, we first identify the instability as a discrepancy between the solver dynamics and stable reverse diffusion dynamics learned by the diffusion model, and show that reducing this gap stabilizes the solver. Building on this, we introduce \textit{Measurement-Consistent Langevin Corrector (MCLC)}, a theoretically grounded plug-and-play stabilization module that remedies the LDM-based inverse problem solvers through measurement-consistent Langevin updates. Compared to prior approaches that rely on linear manifold assumptions, which often fail to hold in latent space, MCLC provides a principled stabilization mechanism, leading to more stable and reliable behavior in latent space.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。