Regularization plays a vital role in achieving complete answers of complex inverse problems and in resolving ill conditioning associated with factors such as limited data and uncertainties of the experimental environment. Recent advances have been made on developing different types of regularizers based on prior knowledge of the unknown parameters. Alternatively, Observations obtained from multiple modalities provide another form of regularizer for improved solution and incorporation of available knowledge, whether as a result of complementary information or as a means to lower measurement noise. This minisymposium brings together experts from the areas of optimization, numerical methods, and a wide range of imaging applications to discuss new regularizing approaches and identify promising future research directions.

3D x-ray imaging beyond the depth of focus limit

Marc Aurèle Gilles (Cornell University)

Reducing the effects of bad data measurements using variance based weighted joint sparsity

Theresa Scarnati (Air Force Research Laboratory)

Plug-and-Play Unplugged: Optimization Free Regularization using Consensus Equilibrium

Charles Bouman (Purdue University)

Learning better models for inverse problems in imaging

Thomas Pock (Graz University of Technology)

Optimization Problems with Sparsity-Inducing Terms

Nadav Hallak (Technion - Israel Institute of Technology)

High-resolution x-ray imaging from sparse, incomplete and uncertain data

Doga Gursoy (Argonne National Laboratory)

Spectral approximation of fractional PDEs in image processing and phase field modeling

Harbir Antil (George Mason University)

Blind Image Fusion for Hyperspectral Imaging with Directional Total Variation

Leon Bungert (University of Münster)

Organizers:

Zichao (Wendy) Di (Argonne National Lab)

Marc Aurèle Gilles (Cornell University)

Keywords:

computed tomography, image compression, image enhancement, image reconstruction, inverse problems, nonlinear optimization