无需真值即可评估图像重建模型，高效判断模型优劣与错误
Bayesian model selection and misspecification testing in imaging inverse problems only from noisy and partial measurements
- 结合贝叶斯交叉验证与随机测量分割，实现无监督模型评估
- 在多种误设情况下准确选出最优模型，计算成本低
- 适用于扩散模型、插件式采样等现代图像先验方法
现代成像技术广泛依赖贝叶斯统计模型解决图像重建与恢复难题。本文针对真实图像不可得场景，提出一种客观评估贝叶斯模型的方法，聚焦模型选择与误设诊断。现有无监督评估方法因计算成本高且不兼容基于机器学习定义的隐式图像先验，难以应用于计算成像。本文提出一种通用方法，融合贝叶斯交叉验证与数据分裂（data fission）——一种随机测量分割技术，适用于任意贝叶斯成像采样器，包括扩散模型和插件式采样器。通过多种评分规则和模型误设类型实验，该方法在低计算开销下实现了优异的模型选择与误设检测精度。
原文摘要 · Abstract (English)
Modern imaging techniques heavily rely on Bayesian statistical models to address difficult image reconstruction and restoration tasks. This paper addresses the objective evaluation of such models in settings where ground truth is unavailable, with a focus on model selection and misspecification diagnosis. Existing unsupervised model evaluation methods are often unsuitable for computational imaging due to their high computational cost and incompatibility with modern image priors defined implicitly via machine learning models. We herein propose a general methodology for unsupervised model selection and misspecification detection in Bayesian imaging sciences, based on a novel combination of Bayesian cross-validation and data fission, a randomized measurement splitting technique. The approach is compatible with any Bayesian imaging sampler, including diffusion and plug-and-play samplers. We demonstrate the methodology through experiments involving various scoring rules and types of model misspecification, where we achieve excellent selection and detection accuracy with a low computational cost.
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