Starlet transform in astronomical data processing

Jean Luc Starck, Fionn Murtagh, Mario Bertero

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Citations (Scopus)

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 languageEnglish
Title of host publicationHandbook of Mathematical Methods in Imaging
EditorsOtmar Scherzer
PublisherSpringer New York
Pages2053-2098
Number of pages46
Volume1
ISBN (Electronic)9781493907908
ISBN (Print)9781493907892
DOIs
Publication statusPublished - 30 May 2015
Externally publishedYes

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    Starck, J. L., Murtagh, F., & Bertero, M. (2015). Starlet transform in astronomical data processing. In O. Scherzer (Ed.), Handbook of Mathematical Methods in Imaging (Vol. 1, pp. 2053-2098). Springer New York. https://doi.org/10.1007/978-1-4939-0790-8_34