Dynamic Compressed Sensing for Real-Time Tomographic Reconstruction

PI Robert Hovden, University of Michigan
TV reconstruction of test image with different data-tolerance parameters.

3D characterization of materials at the nano- and mesoscale has become possible with electron and x-ray tomography. To overcome experimental limitations and improve image quality, compressed sensing (CS) algorithms leverage the notion of sparsity to provide superior resolution. However, while CS algorithms provide high resolution, they are computationally expensive and sensitive to additional tuning parameters. To overcome these challenges, this project aims to enable real-time CS tomography by developing a dynamic tomography framework that performs in-situ reconstruction as new data are collected.