Abstract
With the advent of the next-generation radio-interferometric telescopes, like the Square Kilometre Array, novel signal processing methods are needed to provide the expected imaging resolution and sensitivity from extreme amounts of hyper-spectral data. In this context, we propose a generic nonparametric low-rank and joint-sparsity image model for the regularisation of the associated wide-band inverse problem. We pose a convex optimisation problem and propose the use of an efficient algorithmic solver. The proposed optimisation task requires only one tuning parameter, namely the relative weight between the lowrank and joint-sparsity constraints. Our preliminary simulations suggest superior performance of the model with respect to separate single band imaging, as well as to other recently promoted non-parametric wide-band models leveraging convex optimisation.
Original language | English |
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Title of host publication | 2016 24th European Signal Processing Conference (EUSIPCO) |
Publisher | IEEE |
Pages | 388-392 |
Number of pages | 5 |
ISBN (Electronic) | 9780992862657 |
DOIs | |
Publication status | Published - 1 Dec 2016 |
Event | 24th European Signal Processing Conference 2016 - Hilton Budapest, Budapest, Hungary Duration: 29 Aug 2016 → 2 Sept 2016 Conference number: 24 |
Publication series
Name | European Signal Processing Conference (EUSIPCO) |
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Publisher | IEEE |
ISSN (Print) | 2076-1465 |
Conference
Conference | 24th European Signal Processing Conference 2016 |
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Abbreviated title | EUSIPCO 2016 |
Country/Territory | Hungary |
City | Budapest |
Period | 29/08/16 → 2/09/16 |