Recovering temporal image sequences (videos) based on indirect, noisy, or incomplete data is an essential yet challenging task. We specifically consider the case where each data set is missing vital information, which prevents the accurate recovery of the individual images. Although some recent (variational) methods have demonstrated high-resolution image recovery based on jointly recovering sequential images, there remain robustness issues due to parameter tuning and restrictions on the type of the sequential images. Here, we present a method based on hierarchical Bayesian learning for the joint recovery of sequential images that incorporates prior intra- and inter-image information. Our method restores the missing information in each image by "borrowing" it from the other images. As a result, \emph{all} of the individual reconstructions yield improved accuracy. Our method can be used for various data acquisitions and allows for uncertainty quantification. Some preliminary results indicate its potential use for sequential deblurring and magnetic resonance imaging.

翻译：恢复基于间接、含噪或不完整数据的时序图像序列（视频）是一项基础且富有挑战性的任务。我们重点关注每个数据集缺失关键信息导致单幅图像无法精确重建的场景。尽管近期某些（变分）方法通过联合恢复时序图像实现了高分辨率重建，但参数调优及对时序图像类型的限制仍存在鲁棒性问题。本文提出一种基于分层贝叶斯学习的联合恢复方法，该方法整合了图像内与图像间的先验信息。通过从其他图像"借取"缺失信息，各单幅重建结果的精度均得到提升。本方法适用于多种数据采集场景，并可实现不确定性量化。初步结果表明其在时序去模糊及磁共振成像领域具有应用潜力。