The problem of phase retrieval, ie, the recovery of a function given the magnitude of its
Fourier transform, arises in various fields of science and engineering, including electron …

Modern signal processing (SP) methods rely very heavily on probability and statistics to
solve challenging SP problems. SP methods are now expected to deal with ever more …

This paper gives an overview of the majorization-minimization (MM) algorithmic framework,
which can provide guidance in deriving problem-driven algorithms with low computational …

A large number of imaging problems reduce to the optimization of a cost function, with
typical structural properties. The aim of this paper is to describe the state of the art in …

This work proposes block-coordinate fixed point algorithms with applications to nonlinear
analysis and optimization in Hilbert spaces. The asymptotic analysis relies on a notion of …

The goal of this article is to promote the use of fixed point strategies in data science by
showing that they provide a simplifying and unifying framework to model, analyze, and solve …

The ℓ 1/ℓ 2 ratio regularization function has shown good performance for retrieving sparse
signals in a number of recent works, in the context of blind deconvolution. Indeed, it benefits …

We introduce the first AI-based framework for deep, super-resolution, wide-field radio
interferometric imaging and demonstrate it on observations of the ESO 137-006 radio …

Deep learning models have gained great success in many real-world applications.
However, most existing networks are typically designed in heuristic manners, thus these …

Numerous tasks at the core of statistics, learning and vision areas are specific cases of ill-
posed inverse problems. Recently, learning-based (eg, deep) iterative methods have been …