**Reference: Z. Ramzi, P. Ciuciu and J.-L. Starck. “Benchmarking proximal methods acceleration enhancements for CS-acquired MR image analysis reconstruction ”, SPARS, 2019**

# Category: publications_jls

J.-L. Starck’s publications

## Semi-supervised dictionary learning with graph regularization and active points

Posted on by Jean-Luc Starck

Authors: | Khanh-Hung Tran, Fred-Maurice Ngole-Mboula, J-L. Starck |

Journal: | SIAM Journal on Imaging Sciences |

Year: | 2020 |

DOI: | 10.1137/19M1285469 |

Download: | arXiv |

## Abstract

Supervised Dictionary Learning has gained much interest in the recent decade and has shown significant performance improvements in image classification. However, in general, supervised learning needs a large number of labelled samples per class to achieve an acceptable result. In order to deal with databases which have just a few labelled samples per class, semi-supervised learning, which also exploits unlabelled samples in training phase is used. Indeed, unlabelled samples can help to regularize the learning model, yielding an improvement of classification accuracy. In this paper, we propose a new semi-supervised dictionary learning method based on two pillars: on one hand, we enforce manifold structure preservation from the original data into sparse code space using Locally Linear Embedding, which can be considered a regularization of sparse code; on the other hand, we train a semi-supervised classifier in sparse code space. We show that our approach provides an improvement over state-of-the-art semi-supervised dictionary learning methods

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## Deep Learning for space-variant deconvolution in galaxy surveys

Posted on by Jean-Luc Starck

Authors: | Florent Sureau, Alexis Lechat, J-L. Starck |

Journal: | Astronomy and Astrophysics |

Year: | 2020 |

DOI: | 10.1051/0004-6361/201937039 |

Download: | ADS | arXiv |

## Abstract

The deconvolution of large survey images with millions of galaxies requires developing a new generation of methods that can take a space-variant point spread function into account. These methods have also to be accurate and fast. We investigate how deep learning might be used to perform this task. We employed a U-net deep neural network architecture to learn parameters that were adapted for galaxy image processing in a supervised setting and studied two deconvolution strategies. The first approach is a post-processing of a mere Tikhonov deconvolution with closed-form solution, and the second approach is an iterative deconvolution framework based on the alternating direction method of multipliers (ADMM). Our numerical results based on GREAT3 simulations with realistic galaxy images and point spread functions show that our two approaches outperform standard techniques that are based on convex optimization, whether assessed in galaxy image reconstruction or shape recovery. The approach based on a Tikhonov deconvolution leads to the most accurate results, except for ellipticity errors at high signal-to-noise ratio. The ADMM approach performs slightly better in this case. Considering that the Tikhonov approach is also more computation-time efficient in processing a large number of galaxies, we recommend this approach in this scenario.

In the spirit of reproducible research, the codes will be made freely available on the CosmoStat website (http://www.cosmostat.org). The testing datasets will also be provided to repeat the experiments performed in this paper.

## PySAP: Python Sparse Data Analysis Package for Multidisciplinary Image Processing

Posted on by Samuel Farrens

Authors: | S. Farrens, A. Grigis, L. El Gueddari, Z. Ramzi, Chaithya G. R., S. Starck, B. Sarthou, H. Cherkaoui, P.Ciuciu, J-L. Starck |

Journal: | Astronomy and Computing |

Year: | 2020 |

DOI: | 10.1016/j.ascom.2020.100402 |

Download: | ADS | arXiv |

## Abstract

We present the open-source image processing software package PySAP (Python Sparse data Analysis Package) developed for the COmpressed Sensing for Magnetic resonance Imaging and Cosmology (COSMIC) project. This package provides a set of flexible tools that can be applied to a variety of compressed sensing and image reconstruction problems in various research domains. In particular, PySAP offers fast wavelet transforms and a range of integrated optimisation algorithms. In this paper we present the features available in PySAP and provide practical demonstrations on astrophysical and magnetic resonance imaging data.

## Euclid: The reduced shear approximation and magnification bias for Stage IV cosmic shear experiments

Posted on by Valeria Pettorino

### Euclid: The reduced shear approximation and magnification bias for Stage IV cosmic shear experiments

Authors: | A.C. Deshpande, ..., S. Casas, M. Kilbinger, V. Pettorino, S. Pires, J.-L. Starck, F. Sureau, et al. |

Journal: | Astronomy and Astrophysics |

Year: | 2020 |

DOI: | 10.1051/0004-6361/201937323 |

Download: |

## Abstract

## Euclid preparation: VI. Verifying the Performance of Cosmic Shear Experiments

Posted on by Samuel Farrens

### Euclid preparation: VI. Verifying the Performance of Cosmic Shear Experiments

Authors: | Euclid Collaboration, P. Paykari, ..., S. Farrens, M. Kilbinger, V. Pettorino, S. Pires, J.-L. Starck, F. Sureau, et al. |

Journal: | Astronomy and Astrophysics |

Year: | 2020 |

DOI: | 10.1051/0004-6361/201936980 |

Download: |

## Abstract

## Benchmarking MRI Reconstruction Neural Networks on Large Public Datasets

Posted on by Jean-Luc Starck

**Reference: Z. Ramzi, P. Ciuciu and J.-L. Starck. “Benchmarking MRI reconstruction neural networks on large public datasets ”, Applied Sciences, 10, 1816, 2020. doi:10.3390/app10051816**

## Constraining neutrino masses with weak-lensing multiscale peak counts

Posted on by Virginia Ajani

### Constraining neutrino masses with weak-lensing multiscale peak counts

**Reference: Virginia Ajani, Austin Peel, Valeria Pettorino, Jean-Luc Starck, Zack Li, Jia Liu, 2020. More details in the paper**

## The first Deep Learning reconstruction of dark matter maps from weak lensing observational data

Posted on by Jean-Luc Starck

### DeepMass: The first Deep Learning reconstruction of dark matter maps from weak lensing observational data (DES SV weak lensing data)

### DeepMass

This is the first reconstruction of dark matter maps from weak lensing observational data using deep learning. We train a convolution neural network (CNN) with a Unet based architecture on over 3.6 x 10^5 simulated data realisations with non-Gaussian shape noise and with cosmological parameters varying over a broad prior distribution. Our DeepMass method is substantially more accurate than existing mass-mapping methods. With a validation set of 8000 simulated DES SV data realisations, compared to Wiener filtering with a fixed power spectrum, the DeepMass method improved the mean-square-error (MSE) by 11 per cent. With N-body simulated MICE mock data, we show that Wiener filtering with the optimal known power spectrum still gives a worse MSE than our generalised method with no input cosmological parameters; we show that the improvement is driven by the non-linear structures in the convergence. With higher galaxy density in future weak lensing data unveiling more non-linear scales, it is likely that deep learning will be a leading approach for mass mapping with Euclid and LSST.

**Reference 1: ** N. Jeffrey, F. Lanusse, O. Lahav, J.-L. Starck, "Learning dark matter map reconstructions from DES SV weak lensing data", **Monthly Notices of the Royal Astronomical Society, in press, **2019.

## Euclid: Non-parametric point spread function field recovery through interpolation on a Graph Laplacian

Posted on by Jean-Luc Starck

Authors: | M.A. Schmitz, J.-L. Starck, F. Ngole Mboula, N. Auricchio, J. Brinchmann, R.I. Vito Capobianco, R. Clédassou, L. Conversi, L. Corcione, N. Fourmanoit, M. Frailis, B. Garilli, F. Hormuth, D. Hu, H. Israel, S. Kermiche, T. D. Kitching, B. Kubik, M. Kunz, S. Ligori, P.B. Lilje, I. Lloro, O. Mansutti, O. Marggraf, R.J. Massey, F. Pasian, V. Pettorino, F. Raison, J.D. Rhodes, M. Roncarelli, R.P. Saglia, P. Schneider, S. Serrano, A.N. Taylor, R. Toledo-Moreo, L. Valenziano, C. Vuerli, J. Zoubian |

Journal: | submitted to A&A |

Year: | 2019 |

Download: | arXiv |

## Abstract

*Context*. Future weak lensing surveys, such as the Euclid mission, will attempt to measure the shapes of billions of galaxies in order to derive cosmological information. These surveys will attain very low levels of statistical error and systematic errors must be extremely well controlled. In particular, the point spread function (PSF) must be estimated using stars in the field, and recovered with high accuracy.

*Aims*. This paper's contributions are twofold. First, we take steps toward a non-parametric method to address the issue of recovering the PSF field, namely that of finding the correct PSF at the position of any galaxy in the field, applicable to Euclid. Our approach relies solely on the data, as opposed to parametric methods that make use of our knowledge of the instrument. Second, we study the impact of imperfect PSF models on the shape measurement of galaxies themselves, and whether common assumptions about this impact hold true in a Euclid scenario.

*Methods*. We use the recently proposed Resolved Components Analysis approach to deal with the undersampling of observed star images. We then estimate the PSF at the positions of galaxies by interpolation on a set of graphs that contain information relative to its spatial variations. We compare our approach to PSFEx, then quantify the impact of PSF recovery errors on galaxy shape measurements through image simulations.

*Results*. Our approach yields an improvement over PSFEx in terms of PSF model and on observed galaxy shape errors, though it is at present not sufficient to reach the required Euclid accuracy. We also find that different shape measurement approaches can react differently to the same PSF modelling errors.