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修复任务。本文针对无真实数据可用场景下的此类模型客观评估问题，聚焦于模型选择与误设定诊断。现有无监督模型评估方法因计算成本高昂且与基于机器学习模型隐式定义的现代图像先验不兼容，通常难以适用于计算成像领域。我们提出了一种基于贝叶斯交叉验证与随机测量分裂技术（数据分裂）之新颖组合的通用方法论，用于贝叶斯成像科学中的无监督模型选择与误设定检测。该方法与包括扩散采样器和即插即用采样器在内的任意贝叶斯成像采样器兼容。通过涉及不同评分规则及模型误设定类型的实验，我们验证了该方法能在低计算成本下实现优异的选取与检测精度。