research
∙
02/23/2022
Training Adaptive Reconstruction Networks for Inverse Problems
Neural networks are full of promises for the resolution of ill-posed inv...
share
research
∙
08/06/2021
Optimizing full 3D SPARKLING trajectories for high-resolution T2*-weighted Magnetic Resonance Imaging
The Spreading Projection Algorithm for Rapid K-space samplING, or SPARKL...
research
∙
10/05/2020
Off-the-grid data-driven optimization of sampling schemes in MRI
We propose a novel learning based algorithm to generate efficient and ph...
research
∙
05/20/2020
Fast Wavelet Decomposition of Linear Operators through Product-Convolution Expansions
Wavelet decompositions of integral operators have proven their efficienc...
research
∙
04/15/2020
Sampling Rates for â„“^1-Synthesis
This work investigates the problem of signal recovery from undersampled ...
research
∙
12/11/2018
Convex Regularization and Representer Theorems
We establish a result which states that regularizing an inverse problem ...
research
∙
06/26/2018
On Representer Theorems and Convex Regularization
We establish a general principle which states that regularizing an inver...
research
∙
05/10/2014
Hyperspectral pan-sharpening: a variational convex constrained formulation to impose parallel level lines, solved with ADMM
In this paper, we address the issue of hyperspectral pan-sharpening, whi...
research
∙
07/17/2013
