Starlet Transform in Astronomical Data Processing: Application to Source Detection and Image Deconvolution. / Murtagh, Fionn; Starck, Jean-Luc; Bertero, Mario.

Handbook of Mathematical Methods in Imaging. ed. / Otmar Scherzer. Springer, 2011. p. 1489-1531.

Research output: Chapter in Book/Report/Conference proceedingChapter

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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
Pages1489-1531
Number of pages43
DOIs
Publication statusPublished - 2011
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 1948199