2D–1D Wavelet Reconstruction as a Tool for Source Finding in Spectroscopic Imaging Surveys
L. Flöer A C and B. Winkel BA Argelander-Institut für Astronomie, Auf dem Hügel 71, 53121 Bonn, Germany
B Max-Planck-Institut für Radioastronomie, Auf dem Hügel 69, 53121 Bonn, Germany
C Corresponding author. Email: lfloeer@astro.uni-bonn.de
Publications of the Astronomical Society of Australia 29(3) 244-250 https://doi.org/10.1071/AS11042
Submitted: 7 September 2011 Accepted: 2 November 2011 Published: 4 January 2012
Abstract
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 analysis — techniques: image processing — techniques: spectroscopic
References
Atwood, W. B., et al., 2009, ApJ, 697, 1071| Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXnsF2hsbY%3D&md5=2d1144f1cf65e4cb7b8e545999a6fb5eCAS |
Barnes, D. G., et al., 2001, MNRAS, 322, 486
| Crossref | GoogleScholarGoogle Scholar |
Candès, E., Demanet, L., Donoho, D. & Ying, L., 2006, Multiscale Modeling and Simulation, 5, 861
Giovanelli, R., et al., 2005, AJ, 130, 2598
| 1:CAS:528:DC%2BD28XjvFCnsQ%3D%3D&md5=77eef773f102562371a62f14fcf6bb7eCAS |
Holschneider, M., Kronland-Martinet, R., Morlet, J. & Tchamitchian, P., 1989, in Wavelets. Time-Frequency Methods and Phase Space, ed. J.-M. Combes, A. Grossmann & P. Tchamitchian (Berlin: Springer-Verlag), 286
Johnston, S., et al., 2008, ExA, 22, 151
| Crossref | GoogleScholarGoogle Scholar |
Kerp, J., Winkel, B., Ben Bekhti, N., Flöer, L. and Kalberla, P. M. W., 2011, AN, 332, 637
| Crossref | GoogleScholarGoogle Scholar |
Koribalski, B. S. & Staveley-Smith, L., 2009, ASKAP Survey Science Proposal
Koribalski, B. S., et al., 2004, AJ, 128, 16
| 1:CAS:528:DC%2BD2cXmsVagt7Y%3D&md5=7e2f111351d39252b92d0fdc54b66155CAS |
Murtagh, F., Starck, J.-L. and Bijaoui, A., 1995, A&AS, 112, 179
Nyquist, H., 1928, Trans AIEE, 47, 617
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=132
Serra, J., 1982, Image Analysis and Mathematical Morphology (London: Academic Press)
Starck, J.-L. and Bobin, J., 2010, Proceedings of the IEEE, 98, 1021
| Crossref | GoogleScholarGoogle Scholar |
Starck, J.-L., Fadili, J. and Murtagh, F., 2007, IEEE T Image Process, 16, 297
Starck, J.-L., Fadili, J. M., Digel, S., Zhang, B. and Chiang, J., 2009, A&A, 504, 641
| 1:CAS:528:DC%2BD1MXhtlWltb%2FK&md5=2fe64a2a0de6b31befa1832bb5853536CAS |
Starck, J.-L., Murtagh, F. & Fadili, J. M., 2010, Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity (Cambridge: Cambridge University Press)
Winkel, B., Kalberla, P. M. W., Kerp, J. and Flöer, L., 2010, ApJS, 188, 488
| Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXpt1Oqt78%3D&md5=89f325aff4f99cd368dc8a7afbeb7a82CAS |
Ying, L., Demanet, L. & Candes, E. J., 2005, in Proceedings of SPIE, Volume 5914, Wavelets XI, ed. M. Papadakis, A. F. Laine & M. A. Unser, 351