Compressed sensing imaging techniques for radio interferometry
By Yves Wiaux, Laurent Jacques, Gilles Puy, Anna M. M. Scaife, Pierre Vandergheynst. 2008
278 citations recorded by the source corpus.
Radio interferometry probes astrophysical signals through incomplete and noisy Fourier measurements. The theory of compressed sensing demonstrates that such measurements may actually suffice for accurate reconstruction of sparse or compressible signals. We propose new generic imaging techniques based on convex optimization for global minimization problems defined in this context. The versatility of the framework notably allows introduction of specific prior information on the signals, which offers the possibility of significant improvements of reconstruction relative to the standard local matching pursuit algorithm CLEAN used in radio astronomy. We illustrate the potential of the approach by studying reconstruction performances on simulations of two different kinds of signals observed with very generic interferometric configurations. The first kind is an intensity field of compact astrophysical objects. The second kind is the imprint of cosmic strings in the temperature field of the cosmic microwave background radiation, of particular interest for cosmology.
Subfields: Astronomy and Astrophysics, Computational Mechanics
View Paper: https://www.semanticscholar.org/paper/5e56f505def8be895f3e97ecc83f6fa04d5b7a24
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Citations in the corpus
- Compressed Sensing (2014, unknown): “Estas abarcan diversas áreas como: imagenoloǵıa medica [49, 45], conversión analógico/digital [68], detección mediante RADAR [4, 42], corrección de errores [18], cámaras digitales [34, 21, 51], Sismoloǵıa [41], Radioastronomı́a [71], comunicación inalámbrica [63, 3, 65] y redes de sensores inalámbri”
- EMU: Evolutionary Map of the Universe (2011, methodology): “Other approaches include the use of the Hough transform (Ballard 1981) to measure circular or elliptical structures (such as SN remnants, or radio relic sources), and using compressed sensing imaging techniques to radio interferometric data (Wiaux et al. 2009).”
- An optimized algorithm for multiscale wideband deconvolution of radio astronomical images (2017, background): “Cleaning without a mask to 0.3σ is not possible for this observation, as it causes the clean to diverge at approximately 2σ.”