Starlet Transform in Astronomical Data Processing

Jean-Luc Starck (), Fionn Murtagh () and Mario Bertero ()
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Jean-Luc Starck: CEA/DSM-CNRS-Université Paris Diderot, CEA, IRFU, Service d’Astrophysique, Centre de Saclay, CEA, Laboratoire AIM
Fionn Murtagh: De Montfort University, School of Computer Science and Informatics
Mario Bertero: Università di Genova, DIBRIS

A chapter in Handbook of Mathematical Methods in Imaging, 2015, pp 2053-2098 from Springer

Abstract: Abstract We begin with traditional source detection algorithms in astronomy. We then introduce the sparsity data model. The starlet wavelet transform serves as our main focus in this article. Sparse modeling and noise modeling are described. Applications to object detection and characterization, and to image filtering and deconvolution, are discussed. The multiscale vision model is a further development of this work, which can allow for image reconstruction when the point spread function is not known or not known well. Bayesian and other algorithms are described for image restoration. A range of examples is used to illustrate the algorithms.

Keywords: Point Spread Function; Wavelet Coefficient; Nonnegative Matrix Factorization; Blind Deconvolution; Poisson Noise (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/978-1-4939-0790-8_34

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