Interesting, Compressive Sensing is becoming mainstream in Big Science.
LOFAR Sparse Image Reconstruction by H. Garsden, J. N. Girard, J. L. Starck, S. Corbel, C. Tasse, A. Woiselle, J. McKean, A.S. van Amesfoort, J. Anderson, I. M. Avruch, R. Beck, M. J. Bentum, P. Best, F. Breitling, J. Broderick, M. Brüggen, H. R. Butcher, B. Ciardi, F. de Gasperin, E. de Geus, M. de Vos, S. Duscha, J. Eislöffel, D. Engels, H. Falcke,R. A. Fallows, R. Fender, C. Ferrari, W. Frieswijk, M. A. Garrett, J. Grießmeier, A. W. Gunst, T. E. Hassall,G. Heald, M. Hoeft, J. Hörandel, A. van der Horst, E. Juette, A. Karastergiou, V. I. Kondratiev, M. Kramer,M. Kuniyoshi, G. Kuper, G. Mann, S. Markoff, R. McFadden, D. McKay-Bukowski, D. D. Mulcahy, H. Munk,M. J. Norden, E. Orru, H. Paas, M. Pandey-Pommier, V. N. Pandey, G. Pietka, R. Pizzo, A. G. Polatidis, A. Renting, H. Röttgering, A. Rowlinson, et al. (21 additional authors not shown)
Context. The LOFAR Radio Telescope is a giant digital phased array interferometer with multiple antennas gathered in stations placed throughout Europe. As other interferometers, it provides a discrete set of measured Fourier components of the sky brightness. With these samples, recovering the original brightness distribution with aperture synthesis forms an inverse problem that can be solved by different deconvolution and minimization methods. Aims. Recent papers have established a clear link between the discrete nature of radio interferometry measurement and "compressed sensing" theory, which supports sparse recovery methods to reconstruct an image from the measured visibilities. We aimed at the implementation and at the scientific validation of one of these methods. Methods. We evaluated the photometric and resolution performance of the sparse recovery method in the framework of the LOFAR instrument on simulated and real data. Results. We have implemented a sparse recovery method in the standard LOFAR imaging tools, allowing us to compare the reconstructed images from both simulated and real data with images obtained from classical methods such as CLEAN or MS-CLEAN. Conclusions.We show that i) sparse recovery performs as well as CLEAN in recovering the flux of point sources, ii) performs much better on extended objects (the root mean square error is reduced by a factor up to 10), and iii) provides a solution with an effective angular resolution 2-3 times better than the CLEAN map. Applied to a real LOFAR dataset, the sparse recovery has been validated with the correct photometry and realistic recovered structures of Cygnus A, as compared to other methods. Sparse recovery has been implemented as an image recovery method for the LOFAR Radio Telescope and it can be used for other radio interferometers.
The attendant CS code (SASIR) is here.
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.