Rethinking data-driven point spread function modeling with a differentiable optical model
Authors: Tobias Liaudat, Jean-Luc Starck, Martin Kilbinger, Pierre-Antoine Frugier Journal: Inverse Problems Year:…
Category: Statistical Methods Publications
Publications related to signal processing and statistical methods.
Deep Learning-based galaxy image deconvolution
Authors: Utsav Akhaury, Jean-Luc Starck, Pascale Jablonka, Frédéric Courbin, Kevin Michalewicz Journal: A&A Year: 2022 DOI: …
Density Compensated Unrolled Networks for Non-Cartesian MRI Reconstruction
Deep neural networks have recently been thoroughly investigated as a powerful tool for MRI…
State-of-the-art Machine Learning MRI Reconstruction in 2020: Results of the Second fastMRI Challenge
Accelerating MRI scans is one of the principal outstanding problems in the MRI research…
Faster and better sparse blind source separation through mini-batch optimization
Sparse Blind Source Separation (sBSS) plays a key role in scientific domains as different…
Multi-CCD Point Spread Function Modelling
Context. Galaxy imaging surveys observe a vast number of objects that are affected by…
XPDNet for MRI Reconstruction: an Application to the fastMRI 2020 Brain Challenge
We present a modular cross-domain neural network the XPDNet and its application to the…
Denoising Score-Matching for Uncertainty Quantification in Inverse Problems
Deep neural networks have proven extremely efficient at solving a wide range of inverse…
Wavelets in the Deep Learning Era
Sparsity based methods, such as wavelets, have been state-of-the-art for more than 20 years…
Benchmarking Deep Nets MRI Reconstruction Models on the FastMRI Publicly Available Dataset
The MRI reconstruction field lacked a proper data set that allowed for reproducible…