@InProceedings{7760276,
  author = {A. Abdulaziz and A. Dabbech and A. Onose and Y. Wiaux},
  booktitle = {2016 24th European Signal Processing Conference (EUSIPCO)},
  title = {A low-rank and joint-sparsity model for hyper-spectral radio-interferometric imaging},
  year = {2016},
  pages = {388-392},
  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 non-parametric 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 low-rank 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.},
  keywords = {astronomical image processing;image resolution;inverse problems;optimisation;radiowave interferometry;low-rank and joint-sparsity model;hyper-spectral radio-interferometric imaging;next-generation radio-interferometric telescopes;square kilometre array;hyper-spectral data;convex optimisation problem;associated wide-band inverse problem;single band imaging;convex optimisation;Imaging;Frequency measurement;Minimization;Optimization;Dictionaries;Context;Brightness;hyper-spectral image processing;radio-interferometry},
  doi = {10.1109/EUSIPCO.2016.7760276},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2016/papers/1570256536.pdf},
}

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Onose and Y. Wiaux},\n booktitle = {2016 24th European Signal Processing Conference (EUSIPCO)},\n title = {A low-rank and joint-sparsity model for hyper-spectral radio-interferometric imaging},\n year = {2016},\n pages = {388-392},\n 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 non-parametric 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 low-rank and joint-sparsity constraints. 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