Hostname: page-component-76fb5796d-x4r87 Total loading time: 0 Render date: 2024-04-25T21:28:26.728Z Has data issue: false hasContentIssue false
  • English
  • Français

2D–1D Wavelet Reconstruction as a Tool for Source Finding in Spectroscopic Imaging Surveys

Published online by Cambridge University Press:  02 January 2013

L. Flöer  and
B. Winkel
L. Flöer*
Affiliation:
Argelander-Institut für Astronomie, Auf dem Hügel 71, 53121 Bonn, Germany
B. Winkel
Affiliation:
Max-Planck-Institut für Radioastronomie, Auf dem Hügel 69, 53121 Bonn, Germany
*
CCorresponding author. Email: lfloeer@astro.uni-bonn.de
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Today, image denoising by thresholding of wavelet coefficients is a commonly used tool for 2D image enhancement. Since the data product of spectroscopic imaging surveys has two spatial dimensions and one spectral dimension, the techniques for denoising have to be adapted to this change in dimensionality. In this paper we will review the basic method of denoising data by thresholding wavelet coefficients and implement a 2D–1D wavelet decomposition to obtain an efficient way of denoising spectroscopic data cubes. We conduct different simulations to evaluate the usefulness of the algorithm as part of a source finding pipeline.

Keywords

methods: data analysistechniques: image processingtechniques: spectroscopic
Type
Research Article
Information
Publications of the Astronomical Society of Australia , Volume 29 , Special Issue 3: Source Finding and Visualisation , 2012 , pp. 244 - 250
Copyright
Copyright © Astronomical Society of Australia 2012

References

Atwood, W. B., et al. , 2009, ApJ, 697, 1071CrossRefGoogle Scholar
Barnes, D. G., et al. , 2001, MNRAS, 322, 486CrossRefGoogle Scholar
Candès, E., Demanet, L., Donoho, D. & Ying, L., 2006, Multiscale Modeling and Simulation, 5, 861CrossRefGoogle Scholar
Giovanelli, R., et al. , 2005, AJ, 130, 2598CrossRefGoogle Scholar
Holschneider, M., Kronland-Martinet, R., Morlet, J. & Tchamitchian, P., 1989, in Wavelets. Time-Frequency Methods and Phase Space, ed. J.-M., Combes, A., Grossmann & Tchamitchian, P. (Berlin: Springer-Verlag), 286CrossRefGoogle Scholar
Johnston, S., et al. , 2008, ExA, 22, 151Google Scholar
Kerp, J., Winkel, B., BenBekhti, N., Flöer, L. & Kalberla, P. M. W., 2011, AN, 332, 637Google Scholar
Koribalski, B. S. & Staveley-Smith, L., 2009, ASKAP Survey Science ProposalGoogle Scholar
Koribalski, B. S., et al. , 2004, AJ, 128, 16CrossRefGoogle Scholar
Murtagh, F., Starck, J.-L. & Bijaoui, A., 1995, A&AS, 112, 179Google Scholar
Nyquist, H., 1928, Trans AIEE, 47, 617Google Scholar
Oosterloo, T., et al. , 2009, in Proceedings of Wide Field Astronomy & Technology for the Square Kilometre Array (SKADS 2009), 4–6 November 2009, Chateau de Limelette, Belgium, available at http://pos.sissa.it/cgi-bin/reader/conf.cgi?confid=132Google Scholar
Serra, J., 1982, Image Analysis and Mathematical Morphology (London: Academic Press)Google Scholar
Starck, J.-L. & Bobin, J., 2010, Proceedings of the IEEE, 98, 1021CrossRefGoogle Scholar
Starck, J.-L., Fadili, J. & Murtagh, F., 2007, IEEET Image Process, 16, 297CrossRefGoogle Scholar
Starck, J.-L., Fadili, J. M., Digel, S., Zhang, B. & Chiang, J., 2009, A&A, 504, 641Google Scholar
Starck, J.-L., Murtagh, F. & Fadili, J. M., 2010, Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity (Cambridge: Cambridge University Press)CrossRefGoogle Scholar
Winkel, B., Kalberla, P. M. W., Kerp, J. & Flöer, L., 2010, ApJS, 188, 488CrossRefGoogle Scholar
Ying, L., Demanet, L. & Candes, E. J., 2005, in Proceedings of SPIE, Volume 5914, Wavelets XI, ed. Papadakis, M., Laine, A. F. & Unser, M. A., 351Google Scholar