3 papers
math.NA2025
Priorconditioned Sparsity-Promoting Projection Methods for Deterministic and Bayesian Linear Inverse Problems
Jonathan Lindbloom, Mirjeta Pasha, Jan Glaubitz +1
High-quality reconstructions of signals and images with sharp edges are needed in a wide range of applications. To overcome the large dimensionality of the parameter space and the…
math.NA2024
Complex-Valued Signal Recovery using the Bayesian LASSO
Dylan Green, Jonathan Lindbloom, Anne Gelb
Recovering complex-valued image recovery from noisy indirect data is important in applications such as ultrasound imaging and synthetic aperture radar. While there are many effecti…
math.NA2024
Generalized sparsity-promoting solvers for Bayesian inverse problems: Versatile sparsifying transforms and unknown noise variances
Jonathan Lindbloom, Jan Glaubitz, Anne Gelb
Bayesian hierarchical models can provide efficient algorithms for finding sparse solutions to ill-posed inverse problems. The models typically comprise a conditionally Gaussian pri…