Dynamic Tomography Reconstruction via Low-Rank Modeling with a RED Spatial Prior

Indynamic imaging, a spatiotemporal object is reconstructed from its inconsistent and undersampled measurements obtained over time. Specifically in dynamic tomography, these measurements correspond to the projections of the time-varying object. In this study, we use the dynamic imaging algorithm RED-PSM, which combines the low-rank partially separable modeling (PSM) with the popular Regularization by Denoising (RED) framework and achieves impressive results in different dynamic scenarios, on the tomographic imaging of materials under dynamic mechanical stresses. We compare RED-PSM with a recent deep prior-based technique and with a PSM method with a simple total variation regularization, and demonstrate the advantages of RED-PSM.