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 proceedingChapter


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
Number of pages43
Publication statusPublished - 2011

Cite this