Sequential Image Recovery Using Joint Hierarchical Bayesian Learning

Xiao, Yao; Glaubitz, Jan

doi:10.1007/s10915-023-02234-1

Title: Sequential Image Recovery Using Joint Hierarchical Bayesian Learning

Journal Article · 17 May 2023 · Journal of Scientific Computing

DOI: https://doi.org/10.1007/s10915-023-02234-1 · OSTI ID:1974497

Xiao, Yao;

Abstract 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 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. More precisely, we couple sequential images by penalizing their pixel-wise difference. The corresponding penalty terms (one for each pixel and pair of subsequent images) are treated as weakly-informative random variables that favor small pixel-wise differences but allow occasional outliers. As a result, 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.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: SC0012704

OSTI ID:: 1974497

Journal Information:: Journal of Scientific Computing, Journal Name: Journal of Scientific Computing Journal Issue: 1 Vol. 96; ISSN 0885-7474

Publisher:: Springer Science + Business MediaCopyright Statement

Country of Publication:: United States

Language:: English

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