Sequential Image Recovery Using Joint Hierarchical Bayesian Learning
Name
10915_2023_Article_2234.pdf
Size
5.57 MB
Format
Adobe PDF
Checksum (MD5)
2e8071bd1344d10b5d299b97ea473fba
Author(s)
Xiao, Yao
Glaubitz, Jan
Date Issued
May 18, 2023
Publisher
Springer US
Citation
Journal of Scientific Computing. 2023 May 18;96(1):4
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s10915-023-02234-1
