Starlet Transform in Astronomical Data Processing

  • Jean-Luc Starck
  • Fionn Murtagh
  • Mario Bertero
Living reference work entry


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.


Point Spread Function Wavelet Coefficient Nonnegative Matrix Factorization Blind Deconvolution Poisson Noise 
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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.CEA, Laboratoire AIM CEA/DSM-CNRS-Université Paris Diderot, CEA, IRFU, Service d’Astrophysique, Centre de SaclayGif-Sur-Yvette CedexFrance
  2. 2.School of Computer Science and Informatics De Montfort UniversityLeicesterUK
  3. 3.DIBRIS Università di GenovaGenovaItaly

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