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Starlet Transform in Astronomical Data Processing

In: Handbook of Mathematical Methods in Imaging

Author

Listed:
  • Jean-Luc Starck

    (CEA/Saclay)

  • Fionn Murtagh

    (Science Foundation Ireland)

  • Mario Bertero

    (Università diGenova)

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.

Suggested Citation

  • Jean-Luc Starck & Fionn Murtagh & Mario Bertero, 2011. "Starlet Transform in Astronomical Data Processing," Springer Books, in: Otmar Scherzer (ed.), Handbook of Mathematical Methods in Imaging, chapter 34, pages 1489-1531, Springer.
    • Handle: RePEc:spr:sprchp:978-0-387-92920-0_34
    DOI: 10.1007/978-0-387-92920-0_34
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