对比生成式先验与优化方法在图像逆问题中的稳定性表现
A Stability Benchmark of Generative Regularizers for Inverse Problems
- 用数值实验评估生成模型在逆问题中的收敛性与鲁棒性
- 发现生成先验在部分场景下表现优异，但对噪声和算子误差敏感
- 为医学影像等高可靠性场景提供选型参考
生成式（扩散）先验在图像逆问题中表现出色。然而，在科学与医学成像中，重建方法必须在不完美条件下保持稳定可靠。稳定性通常包含收敛正则化、对分布外数据的鲁棒性，以及对前向算子或噪声模型误差的容忍度。本文通过数值实验评估这些性质，并将生成方法与基于现代优化技术的变分方法进行对比。结果揭示了生成先验在哪些设置和应用中能实现顶尖重建效果，也指出了其可能失效甚至引发问题的场景。
原文摘要 · Abstract (English)
Generative (diffusion) priors demonstrate remarkable performance in addressing inverse problems in imaging. Yet, for scientific and medical imaging, it is crucial that reconstruction techniques remain stable and reliable under imperfect settings. Typical definitions of stability encompass the notion of ''convergent regularization'', robustness to out-of-distribution data, and to inaccuracies in the forward operator or noise model. We evaluate these properties numerically. Furthermore, we benchmark generative approaches against modern optimization-based methods inspired by the widely used variational techniques. Our results give insights for which settings and applications generative priors can deliver state-of-the-art reconstructions, and on those in which they fall short or may even be problematic.
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