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.Comment: Submitted to IEEE Special Issue on Signal Processin