Deep learning for a space-variant deconvolution in galaxy surveys

  title={Deep learning for a space-variant deconvolution in galaxy surveys},
  author={Florent Sureau and Alexis Lechat and Jean-Luc Starck},
  journal={Astronomy and Astrophysics},
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… Expand
Galaxy Image Restoration with Shape Constraint
Images acquired with a telescope are blurred and corrupted by noise. The blurring is usually modeled by a convolution with the Point Spread Function and the noise by Additive Gaussian Noise.Expand
Blind multi-frame deconvolution for the correction of space-variant blur in images
A novel approach can handle large translations in the local PSFs, hence the algorithm is able to correct for morph in the images and can be applied in situations where the signal-to-noise ratio is low. Expand
General framework for cosmological dark matter bounds using N -body simulations
We present a general framework for obtaining robust bounds on the nature of dark matter using cosmological $N$-body simulations and Lyman-alpha forest data. We construct an emulator of hydrodynamicalExpand
SLITRONOMY: Towards a fully wavelet-based strong lensing inversion technique
Strong gravitational lensing provides a wealth of astrophysical information on the baryonic and dark matter content of galaxies. It also serves as a valuable cosmological probe by allowing us toExpand


Space variant deconvolution of galaxy survey images
Removing the aberrations introduced by the point spread function (PSF) is a fundamental aspect of astronomical image processing. The presence of noise in observed images makes deconvolution aExpand
An improved cosmological parameter inference scheme motivated by deep learning
Dark matter cannot be observed directly, but its weak gravitational lensing slightly distorts the apparent shapes of background galaxies, making weak lensing one of the most promising probes ofExpand
Solving ill-posed inverse problems using iterative deep neural networks
The method builds on ideas from classical regularization theory and recent advances in deep learning to perform learning while making use of prior information about the inverse problem encoded in the forward operator, noise model and a regularizing functional to results in a gradient-like iterative scheme. Expand
Deep Unfolding of a Proximal Interior Point Method for Image Restoration
iRestNet is developed, a neural network architecture obtained by unfolding a proximal interior point algorithm that compares favorably with both state-of-the-art variational and machine learning methods in terms of image quality. Expand
Euclid: Nonparametric point spread function field recovery through interpolation on a graph Laplacian
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 attainExpand
Deep Convolutional Neural Network for Inverse Problems in Imaging
The proposed network outperforms total variation-regularized iterative reconstruction for the more realistic phantoms and requires less than a second to reconstruct a <inline-formula> <tex-math notation="LaTeX">$512\times 512$ </tex- math></inline- formula> image on the GPU. Expand
The Little Engine That Could: Regularization by Denoising (RED)
This paper provides an alternative, more powerful, and more flexible framework for achieving Regularization by Denoising (RED): using the denoising engine in defining the regulariza... Expand
CNN-Based Projected Gradient Descent for Consistent CT Image Reconstruction
A relaxed version of PGD wherein gradient descent enforces measurement consistency, while a CNN recursively projects the solution closer to the space of desired reconstruction images and shows an improvement over total variation-based regularization, dictionary learning, and a state-of-the-art deep learning-based direct reconstruction technique. Expand
GREAT3 results - I. Systematic errors in shear estimation and the impact of real galaxy morphology
We present first results from the third GRavitational lEnsing Accuracy Testing (GREAT3) challenge, the third in a sequence of challenges for testing methods of inferring weak gravitational lensingExpand
The GRavitational lEnsing Accuracy Testing 3 (GREAT3) challenge is the third in a series of image analysis challenges, with a goal of testing and facilitating the development of methods for analyzingExpand