We consider linear inverse problems where the solution is assumed to have a sparse
expansion on an arbitrary preassigned orthonormal basis. We prove that replacing the usual …

This paper describes a new MATLAB software package of iterative regularization methods
and test problems for large-scale linear inverse problems. The software package, called IR …

We study the problem of multi-focus image fusion, where the key challenge is detecting the
focused regions accurately among multiple partially focused source images. Inspired by the …

Parameter Estimation and Inverse Problems, Third Edition, is structured around a course at
New Mexico Tech and is designed to be accessible to typical graduate students in the …

Quantitative susceptibility mapping (QSM) based on gradient echo (GRE) magnetic
resonance phase data is a novel technique for non-invasive assessment of magnetic tissue …

A real-world guide to practical applications of ground penetrating radar (GPR) The
nondestructive nature of ground penetrating radar makes it an important and popular …

Computational microscopy, as a subfield of computational imaging, combines optical
manipulation and image algorithmic reconstruction to recover multi-dimensional microscopic …

The field of inverse problems has experienced explosive growth in the last few decades.
This is due in part to the importance of applications, like biomedical and seismic imaging …

Iterative shrinkage/thresholding (1ST) algorithms have been recently proposed to handle a
class of convex unconstrained optimization problems arising in image restoration and other …

Since the realization that diffusion MRI can probe the microstructural organization and
orientation of biological tissue in vivo and non‐invasively, a multitude of diffusion imaging …