Photoacoustic computed tomography (PACT), also known as optoacoustic tomography, is
an emerging imaging technique that holds great promise for biomedical imaging. PACT is a …

Recovering a function or high-dimensional parameter vector from indirect measurements is
a central task in various scientific areas. Several methods for solving such inverse problems …

Over the last decades, the total variation (TV) has evolved to be one of the most broadly-
used regularisation functionals for inverse problems, in particular for imaging applications …

Regularization by denoising (RED), as recently proposed by Romano, Elad, and Milanfar, is
powerful image-recovery framework that aims to minimize an explicit regularization objective …

Correction to: Convex Analysis and Monotone Operator Theory in Hilbert Spaces Page 1
Correction to: Convex Analysis and Monotone Operator Theory in Hilbert Spaces Correction …

Sparse estimation methods are aimed at using or obtaining parsimonious representations of
data or models. They were first dedicated to linear variable selection but numerous …

An up-to-date account of the interplay between optimization and machine learning,
accessible to students and researchers in both communities. The interplay between …

Most algorithms for solving optimization problems or finding saddle points of convex-
concave functions are fixed-point algorithms. In this work we consider the generic problem of …

Model selection and parameter estimation are important for the effective integration of
experimental data, scientific theory, and precise simulations. In this work, we develop a …

Inverse problems in image processing are typically cast as optimization tasks, consisting of
data fidelity and stabilizing regularization terms. A recent regularization strategy of great …