Multiscale CLEAN Deconvolution of Radio Synthesis Images

By Tim J. Cornwell. IEEE Journal on Selected Topics in Signal Processing, 2008

302 citations recorded by the source corpus.

Radio synthesis imaging is dependent upon deconvolution algorithms to counteract the sparse sampling of the Fourier plane. These deconvolution algorithms find an estimate of the true sky brightness from the necessarily incomplete sampled visibility data. The most widely used radio synthesis deconvolution method is the CLEAN algorithm of Hogbom. This algorithm works extremely well for collections of point sources and surprisingly well for extended objects. However, the performance for extended objects can be improved by adopting a multiscale approach. We describe and demonstrate a conceptually simple and algorithmically straightforward extension to CLEAN that models the sky brightness by the summation of components of emission having different size scales. While previous multiscale algorithms work sequentially on decreasing scale sizes, our algorithm works simultaneously on a range of specified scales. Applications to both real and simulated data sets are given.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/7d456ca9902e3997e3e0069e59502e3a4e66d86d

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Citations in the corpus

LOFAR 150-MHz observations of the Boötes field: catalogue and source counts (2016, methodology): “Multiscale CLEAN (MS-MFS; Rau & Cornwell 2011; Cornwell 2008) was used only in the case of a few complex extended sources.”
An optimized algorithm for multiscale wideband deconvolution of radio astronomical images (2017, methodology): “3 to calculate the scale bias in WSCLEAN, rather than the scale-bias function given by Cornwell (2008).”
FIRST RESULTS FROM HIGH ANGULAR RESOLUTION ALMA OBSERVATIO NS TOWARD THE HL TAU REGION (2015, methodology): “Additionally, weused themultiscale imaging option (Cornwell 2008) for all three bands with scales of 0, 5, and 15, approximately corresponding to 0, 1, and 3× the synthesized beam.”
Compressed sensing imaging techniques for radio interferometry (2008, methodology): “It was also very recently acknowledged in radio astronomy (Cornwell 2008).”
Molecular and atomic gas in the Local Group galaxy M 33 (2010, methodology): “The imaging was achieved using the multi-scale cleanMSCLEAN algorithm described in Cornwell (2008).”
wsclean: an implementation of a fast, generic wide-field imager for radio astronomy (2014, methodology): “Results of applying CASA’s multi-scale clean (Cornwell 2008) on MWA data show that especially the Galactic plane is significantly better deconvolved using multi-scale clean, but it is very computationally expensive.”
(Almost) Dark Galaxies in the ALFALFA Survey: Isolated H i-bearing Ultra-diffuse Galaxies (2017, methodology): “Since we expected the source to be extended, we used the multiscale clean option which improves localization of extended flux (Cornwell 2008).”
KILOPARSEC-SCALE DUST DISKS IN HIGH-REDSHIFT LUMINOUS SUBMILLIMETER GALAXIES (2016, unknown): “For the untapered maps, multi-scale clean (Cornwell 2008) was employed using scales of [0′′, 0.3′′, 0.6′′, 1.2′′].”
LOFAR FACET CALIBRATION (2016, methodology): “Multi-scale7 clean (Cornwell 2008) is employed for a few complex extended sources (Figure 10).”
The Disk Substructures at High Angular Resolution Project (DSHARP). I. Motivation, Sample, Calibration, and Overview (2018, methodology): “We used the multi-scale, multifrequency synthesis (assuming a flat spectrum) deconvolution mode (Cornwell 2008) with a Briggs robust=0.5 weighting scheme.”