Jean-Luc Starck | DeepAI

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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 ...

0 Benjamin Naoto Chiche, et al. ∙

∙ 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...

0 Tobias Liaudat, et al. ∙

∙ 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...

0 Benjamin Remy, et al. ∙

∙ 07/12/2022

Hybrid Physical-Neural ODEs for Fast N-body Simulations

We present a new scheme to compensate for the small-scales approximation...

0 Denise Lanzieri, et al. ∙

∙ 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...

0 Tobias Liaudat, et al. ∙

∙ 01/14/2022

Probabilistic Mass Mapping with Neural Score Estimation

Weak lensing mass-mapping is a useful tool to access the full distributi...

5 Benjamin Remy, et al. ∙

∙ 12/16/2021

Stable Long-Term Recurrent Video Super-Resolution

Recurrent models have gained popularity in deep learning (DL) based vide...

0 Benjamin Naoto Chiche, et al. ∙

∙ 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...

0 Tobias Liaudat, et al. ∙

∙ 06/01/2021

Is good old GRAPPA dead?

We perform a qualitative analysis of performance of XPDNet, a state-of-t...

0 Zaccharie Ramzi, et al. ∙

∙ 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...

0 Zaccharie Ramzi, et al. ∙

∙ 02/23/2021

Deep Unrolled Network for Video Super-Resolution

Video super-resolution (VSR) aims to reconstruct a sequence of high-reso...

0 Benjamin Naoto Chiche, et al. ∙

∙ 01/05/2021

Density Compensated Unrolled Networks for Non-Cartesian MRI Reconstruction

Deep neural networks have recently been thoroughly investigated as a pow...

0 Zaccharie Ramzi, et al. ∙

∙ 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...

0 Matthew J. Muckley, et al. ∙

∙ 11/16/2020

Denoising Score-Matching for Uncertainty Quantification in Inverse Problems

Deep neural networks have proven extremely efficient at solving a wide r...

0 Zaccharie Ramzi, et al. ∙

∙ 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...

0 Zaccharie Ramzi, et al. ∙

∙ 09/13/2020

Manifold attack

Machine Learning in general and Deep Learning in particular has gained m...

0 Khanh-Hung Tran, et al. ∙

∙ 09/13/2020

Semi-supervised dictionary learning with graph regularization and active points

Supervised Dictionary Learning has gained much interest in the recent de...

0 Khanh-Hung Tran, et al. ∙

∙ 11/01/2019

Deep Learning for space-variant deconvolution in galaxy surveys

Deconvolution of large survey images with millions of galaxies requires ...

7 Florent Sureau, et al. ∙

∙ 12/11/2018

Semi-supervised dual graph regularized dictionary learning

In this paper, we propose a semi-supervised dictionary learning method t...

0 Khanh-Hung Tran, et al. ∙

∙ 10/25/2018

On the dissection of degenerate cosmologies with machine learning

Based on the DUSTGRAIN-pathfinder suite of simulations, we investigate o...

0 Julian Merten, et al. ∙

∙ 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 ...

0 Morgan A. Schmitz, et al. ∙

∙ 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...

0 Jérôme Bobin, et al. ∙

∙ 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...

0 Fred Maurice Ngolè Mboula, et al. ∙

∙ 07/29/2014

NMF with Sparse Regularizations in Transformed Domains

Non-negative blind source separation (non-negative BSS), which is also r...

0 Jeremy Rapin, et al. ∙

∙ 08/26/2013

Sparse and Non-Negative BSS for Noisy Data

Non-negative blind source separation (BSS) has raised interest in variou...

0 Jeremy Rapin, et al. ∙

∙ 04/12/2013

Astronomical Image Denoising Using Dictionary Learning

Astronomical images suffer a constant presence of multiple defects that ...

0 Simon Beckouche, et al. ∙

∙ 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...

0 Fionn Murtagh, et al. ∙

∙ 02/24/2008

Wavelet and Curvelet Moments for Image Classification: Application to Aggregate Mixture Grading

We show the potential for classifying images of mixtures of aggregate, b...

0 Fionn Murtagh, et al. ∙

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