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06/24/2023
Deep learning-based deconvolution for interferometric radio transient reconstruction
Radio astronomy is currently thriving with new large ground-based radio ...
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06/12/2023
Point spread function modelling for astronomical telescopes: a review focused on weak gravitational lensing studies
The accurate modelling of the Point Spread Function (PSF) is of paramoun...
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10/28/2022
Towards solving model bias in cosmic shear forward modeling
As the volume and quality of modern galaxy surveys increase, so does the...
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07/12/2022
Hybrid Physical-Neural ODEs for Fast N-body Simulations
We present a new scheme to compensate for the small-scales approximation...
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03/09/2022
Rethinking data-driven point spread function modeling with a differentiable optical model
In astronomy, upcoming space telescopes with wide-field optical instrume...
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01/14/2022
Probabilistic Mass Mapping with Neural Score Estimation
Weak lensing mass-mapping is a useful tool to access the full distributi...
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12/16/2021
Stable Long-Term Recurrent Video Super-Resolution
Recurrent models have gained popularity in deep learning (DL) based vide...
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11/24/2021
Rethinking the modeling of the instrumental response of telescopes with a differentiable optical model
We propose a paradigm shift in the data-driven modeling of the instrumen...
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06/01/2021
Is good old GRAPPA dead?
We perform a qualitative analysis of performance of XPDNet, a state-of-t...
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06/01/2021
SHINE: SHaring the INverse Estimate from the forward pass for bi-level optimization and implicit models
In recent years, implicit deep learning has emerged as a method to incre...
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02/23/2021
Deep Unrolled Network for Video Super-Resolution
Video super-resolution (VSR) aims to reconstruct a sequence of high-reso...
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01/05/2021
Density Compensated Unrolled Networks for Non-Cartesian MRI Reconstruction
Deep neural networks have recently been thoroughly investigated as a pow...
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12/09/2020
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 t...
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11/16/2020
Denoising Score-Matching for Uncertainty Quantification in Inverse Problems
Deep neural networks have proven extremely efficient at solving a wide r...
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10/15/2020
XPDNet for MRI Reconstruction: an Application to the fastMRI 2020 Brain Challenge
We present a modular cross-domain neural network the XPDNet and its appl...
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09/13/2020
Manifold attack
Machine Learning in general and Deep Learning in particular has gained m...
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09/13/2020
Semi-supervised dictionary learning with graph regularization and active points
Supervised Dictionary Learning has gained much interest in the recent de...
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11/01/2019
Deep Learning for space-variant deconvolution in galaxy surveys
Deconvolution of large survey images with millions of galaxies requires ...
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12/11/2018
Semi-supervised dual graph regularized dictionary learning
In this paper, we propose a semi-supervised dictionary learning method t...
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10/25/2018
On the dissection of degenerate cosmologies with machine learning
Based on the DUSTGRAIN-pathfinder suite of simulations, we investigate o...
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08/07/2017
Wasserstein Dictionary Learning: Optimal Transport-based unsupervised non-linear dictionary learning
This article introduces a new non-linear dictionary learning method for ...
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12/09/2014
Sparsity and adaptivity for the blind separation of partially correlated sources
Blind source separation (BSS) is a very popular technique to analyze mul...
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10/16/2014
Super-resolution method using sparse regularization for point-spread function recovery
In large-scale spatial surveys, such as the forthcoming ESA Euclid missi...
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07/29/2014
NMF with Sparse Regularizations in Transformed Domains
Non-negative blind source separation (non-negative BSS), which is also r...
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08/26/2013
Sparse and Non-Negative BSS for Noisy Data
Non-negative blind source separation (BSS) has raised interest in variou...
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04/12/2013
Astronomical Image Denoising Using Dictionary Learning
Astronomical images suffer a constant presence of multiple defects that ...
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09/02/2009
Scale-Based Gaussian Coverings: Combining Intra and Inter Mixture Models in Image Segmentation
By a "covering" we mean a Gaussian mixture model fit to observed data. A...
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02/24/2008