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.

翻译：现代成像技术严重依赖贝叶斯统计模型来解决困难的图像重建与复原任务。本文旨在探讨在缺乏真实数据的情况下，对此类模型进行客观评估，重点关注模型选择与错误设定诊断。现有的无监督模型评估方法通常不适用于计算成像领域，因其计算成本高昂且与现代通过机器学习模型隐式定义的图像先验不兼容。我们在此提出一种基于贝叶斯交叉验证与数据裂分（一种随机测量分割技术）新颖组合的通用方法，用于贝叶斯成像科学中的无监督模型选择与错误设定检测。该方法兼容任何贝叶斯成像采样器，包括扩散采样器和即插即用采样器。我们通过涉及多种评分规则和模型错误设定类型的实验验证了该方法的有效性，在低计算成本下实现了优异的模型选择与检测精度。