提出新采样方法，让扩散模型更好融合测量与先验信息。
Measurement-Aligned Sampling for Inverse Problem
- 通过可调权重平衡测量数据与先验信息，灵活适应不同噪声。
- 在非高斯和未知噪声下仍保持测量一致性，性能优于现有方法。
- 适合需要高精度重建的逆问题场景，如医学成像或图像恢复。
扩散模型为解决逆问题提供了强大的方式，可融入复杂先验信息。然而，现有方法难以正确处理先验与测量信号之间的冲突，常无法最大化与测量结果的一致性，尤其在非高斯或未知噪声条件下表现不佳。为此，本文提出测量对齐采样（MAS），一种新的线性逆问题求解框架，能灵活平衡先验与测量信息。MAS统一并扩展了DDNM、TMPD等方法，并可推广至已知高斯噪声及未知或非高斯噪声情形。大量实验表明，MAS在多种任务中持续优于当前最优方法，且计算开销相对较低。
原文摘要 · Abstract (English)
Diffusion models provide a powerful way to incorporate complex prior information for solving inverse problems. However, existing methods struggle to correctly incorporate guidance from conflicting signals in the prior and measurement, and often failed to maximizing the consistency to the measurement, especially in the challenging setting of non-Gaussian or unknown noise. To address these issues, we propose Measurement-Aligned Sampling (MAS), a novel framework for linear inverse problem solving that flexibly balances prior and measurement information. MAS unifies and extends existing approaches such as DDNM, TMPD, while generalizing to handle both known Gaussian noise and unknown or non-Gaussian noise types. Extensive experiments demonstrate that MAS consistently outperforms state-of-the-art methods across a variety of tasks, while maintaining relatively low computational cost.
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