Deep learning for a space-variant deconvolution in galaxy surveys

@article{Sureau2020DeepLF,
  title={Deep learning for a space-variant deconvolution in galaxy surveys},
  author={Florent Sureau and Alexis Lechat and Jean-Luc Starck},
  journal={Astronomy \& Astrophysics},
  year={2020}
}
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… 

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.

Blind multi-frame deconvolution for the correction of space-variant blur in images

TLDR
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.

Semi-Blind Source Separation with Learned Constraints

Deblurring galaxy images with Tikhonov regularization on magnitude domain

We propose a regularization-based deblurring method that works efficiently for galaxy images. The spatial resolution of a ground-based telescope is generally limited by seeing conditions and is

Improvement of cosmological constraints with the cross-correlation between line-of-sight optical galaxy and FRB dispersion measures

Fast Radio Bursts (hereafter FRBs) can be used in cosmology by studying the Dispersion Measure (hereafter DM) as a function of redshift. The large scale structure of matter distribution is regarded

State-of-the-Art Approaches for Image Deconvolution Problems, including Modern Deep Learning Architectures

TLDR
This review has divided deconvolution methods into classical, deep learning-based, and optimization-based methods, and described the difficulties in their application, such as the discrepancy between the standard loss functions and the visual content and the heterogeneity of the images.

MultiWienerNet: Deep Learning for Fast Shift-Varying Deconvolution

Curvaton and quantum gravity effect on the tensor-to-scalar ratio of the chaotic inflation

The expected tensor-to-scalar ratio estimate of the upcoming CMB mission probe measurements may establish a lower value of the ratio than the currently obtained value. It can be described in terms of

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 to

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 hydrodynamical

References

SHOWING 1-10 OF 79 REFERENCES

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 a

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 of

Solving ill-posed inverse problems using iterative deep neural networks

TLDR
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.

Deep unfolding of a proximal interior point method for image restoration

TLDR
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.

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 attain

Deep Convolutional Neural Network for Inverse Problems in Imaging

TLDR
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.

The Little Engine That Could: Regularization by Denoising (RED)

TLDR
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...

CNN-Based Projected Gradient Descent for Consistent CT Image Reconstruction

TLDR
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.

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 lensing

THE THIRD GRAVITATIONAL LENSING ACCURACY TESTING (GREAT3) CHALLENGE HANDBOOK

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 analyzing
...