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. Recovering the observed image is an ill-posed inverse problem. Sparse deconvolution is well known to be an ef...

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Bibliographic Details
Published inarXiv.org
Main Authors Nammour, Fadi, Schmitz, Morgan A, Fred Maurice Ngolè Mboula, Starck, Jean-Luc, Girard, Julien N
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 25.01.2021
Subjects
Algorithms
Blurring
Convolution
Deconvolution
Ellipticity
Galaxies
Image acquisition
Image restoration
Inverse problems
Noise
Physics - Instrumentation and Methods for Astrophysics
Point spread functions
Random noise
Regularization
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Summary: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. Recovering the observed image is an ill-posed inverse problem. Sparse deconvolution is well known to be an efficient deconvolution technique, leading to optimized pixel Mean Square Errors, but without any guarantee that the shapes of objects (e.g. galaxy images) contained in the data will be preserved. In this paper, we introduce a new shape constraint and exhibit its properties. By combining it with a standard sparse regularization in the wavelet domain, we introduce the Shape COnstraint REstoration algorithm (SCORE), which performs a standard sparse deconvolution, while preserving galaxy shapes. We show through numerical experiments that this new approach leads to a reduction of galaxy ellipticity measurement errors by at least 44%.
ISSN:2331-8422
DOI:10.48550/arxiv.2101.10021