Multiscale CLEAN Deconvolution of Radio Synthesis Images

2008/06/13 by T. J. Cornwell · 16 citations
Computer Science · Engineering · Physics and Astronomy · #Advanced Image Processing Techniques #Algorithm #Blind deconvolution #Brightness #Computer science #Computer vision #Deconvolution #Image and Signal Denoising Methods #Optics #Physics #Range (aeronautics) #Sampling (signal processing) #Scale (ratio) #Sparse and Compressive Sensing Techniques #Visibility #astro-ph

paper · pdf · doi:10.1109/jstsp.2008.2006388

Submitted to IEEE Special Issue on Signal Processing

arxiv created 2008/06/13 · openalex publication_date 2008/10/01 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

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.

Citations

• Astronomical image representation by the curvelet transform
• Maximum-entropy image reconstruction using wavelets
• Scale sensitive deconvolution of interferometric images

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