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Starlet Transform in Astronomical Data Processing

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Handbook of Mathematical Methods in Imaging

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 chapter. 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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Author information

Authors and Affiliations

  1. CEA/Saclay, Gif-sur-Yvette Cedex, France

    Jean-Luc Starck

  2. Science Foundation Ireland, Dublin, Ireland

    Fionn Murtagh

  3. Università diGenova, Genova, Italy

    Mario Bertero

Authors
  1. Jean-Luc Starck
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  2. Fionn Murtagh
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  3. Mario Bertero
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Editor information

Editors and Affiliations

  1. Computational Science Center, University of Vienna, Nordbergstrasse 15, Vienna, Austria

    Otmar Scherzer

  2. RICAM, Austrian Academy of Sciences, Linz, Austria

    Otmar Scherzer

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Starck, JL., Murtagh, F., Bertero, M. (2011). Starlet Transform in Astronomical Data Processing. In: Scherzer, O. (eds) Handbook of Mathematical Methods in Imaging. Springer, New York, NY. https://doi.org/10.1007/978-0-387-92920-0_34

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