27th Conference of the International Linear Algebra Society (ILAS 2026)

Bayesian inference for rough feature reconstructions

May 21, 2026, 11:25 AM

25m

Torgersen Hall 1030 (Virginia Tech)

Minisymposium Talk Inverse Problems and Uncertainty Quantification through the Lens of Numerical Linear Algebra Inverse Problems and Uncertainty Quantification through the Lens of Numerical Linear Algebra

Speaker

Prof. Lassi Roininen (LUT University)

Description

Edges in imaging, that is sharp discontinuities in intensity, pose a significant challenge for inverse problems algorithms that often rely on Gaussian assumptions. Non-Gaussian heavy-tailed priors, which can better model the sparsity and sharp transitions inherent in edges, offer an alternative for edge-preserving image reconstructions. We consider the inherent difficulties in handling edges and highlight the potential of heavy-tailed prior models to transform this problem into a practical engineering solution.