MATH Seminar

Title: Data-Driven Methods for Image Reconstruction
Seminar: Numerical Analysis and Scientific Computing
Speaker: Jeff Fessler of University of Michigan
Contact: James Nagy,
Date: 2020-11-06 at 2:40PM
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Inverse problems are usually ill-conditioned or ill-posed, meaning that there are multiple candidate solutions that all fit the measured data equally or reasonably well. Modeling assumptions are needed to distinguish among candidate solutions. This talk will focus on contemporary adaptive signal models and their use as regularizers for solving inverse problems, including methods based on machine-learning tools. Applications illustrated will include MRI and CT.

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