A parallel & automatically tuned algorithm for multispectral image deconvolution

In the era of big data, radio astronomical image reconstruction algorithms are challenged to estimate clean images given limited computing resources and time. This article is driven by the need for large scale image reconstruction for the future Square Kilometre Array (SKA), which will become in the...

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Bibliographic Details
Published in:arXiv.org 2019-08
Main Authors: Ammanouil, Rita, Ferrari, André, David, Mary, Ferrari, Chiara, Loi, Francesca
Format: Article
Language:English
Subjects:
Algorithms
Big Data
Broadband
Computing time
Data management
Deconvolution
Frequencies
Image reconstruction
Point spread functions
Radio sources (astronomy)
Radio telescopes
Regularization
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Summary:In the era of big data, radio astronomical image reconstruction algorithms are challenged to estimate clean images given limited computing resources and time. This article is driven by the need for large scale image reconstruction for the future Square Kilometre Array (SKA), which will become in the next decades the largest low and intermediate frequency radio telescope in the world. 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 in 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.
ISSN:2331-8422
DOI:10.48550/arxiv.1905.08468