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。本文旨在解决在缺乏真实参考数据的场景下对此类模型进行客观评估的问题，重点关注模型选择与误设诊断。现有的无监督模型评估方法往往不适用于计算成像领域，原因在于其计算成本高昂，且与通过机器学习模型隐式定义的现代图像先验不兼容。本文提出了一种适用于贝叶斯成像科学的无监督模型选择与误设检测通用方法，该方法基于贝叶斯交叉验证与数据裂分（一种随机测量分割技术）的创新性结合。该框架兼容包括扩散采样器和即插即用采样器在内的任何贝叶斯成像采样器。我们通过涉及多种评分规则和模型误设类型的实验验证了该方法的有效性，实验结果表明能以较低计算成本实现优异的模型选择与误设检测精度。