Starlet Transform in Astronomical Data Processing: Application to Source Detection and Image Deconvolution

Fionn Murtagh; Jean-Luc Starck; Mario Bertero

doi:10.1007/978-0-387-929200_34

Starlet Transform in Astronomical Data Processing: Application to Source Detection and Image Deconvolution

Fionn Murtagh, Jean-Luc Starck, Mario Bertero

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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.

Original language	English
Title of host publication	Handbook of Mathematical Methods in Imaging
Editors	Otmar Scherzer
Publisher	Springer
Pages	1489-1531
Number of pages	43
DOIs	https://doi.org/10.1007/978-0-387-929200_34
Publication status	Published - 2011

Access to Document

10.1007/978-0-387-929200_34

Cite this

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title = "Starlet Transform in Astronomical Data Processing: Application to Source Detection and Image Deconvolution",

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. ",

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