http://arxiv.org/abs/1905.08468
In the era of big data in the radio astronomical field, image reconstruction algorithms are challenged to estimate clean images given limited computing resources and time. This article is driven by the extensive need for large scale image reconstruction for the future Square Kilometre Array (SKA), the largest low- and intermediate frequency radio telescope of the next decades. This work proposes a scalable wideband deconvolution algorithm called MUFFIN, which stands for `MUlti Frequency image reconstruction For radio INterferometry’. MUFFIN estimates the sky images at various frequency bands given the corresponding dirty images and point spread functions. The reconstruction is achieved by minimizing a data fidelity term and joint spatial and spectral sparse analysis regularization terms. It is consequently non-parametric w.r.t. the spectral behaviour of radio sources. MUFFIN algorithm is endowed with a parallel implementation and an automatic tuning of the regularization parameters, making it scalable and well suited for big data applications such as SKA. Comparisons between MUFFIN and the state-of-the-art wideband reconstruction algorithm are provided.
R. Ammanouil, A. Ferrari, D. Mary, et. al.
Wed, 22 May 19
17/59
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