Multi-Scale CLEAN deconvolution of radio synthesis images

Details Multi-Scale CLEAN deconvolution of radio synthesis images Multi-Scale CLEAN deconvolution of radio synthesis images

2008-06-13

arxiv.org/abs/0806.2228v1

Astronomy and Astrophysics

Astrophysics

Submitted to IEEE Special Issue on Signal Processing

Scientific paper

10.1109/JSTSP.2008.2006388

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 multi-scale 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.

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