Two distributed algorithms for the deconvolution of large radio-interferometric multispectral images. Meillier, C., Bianchi, P., & Hachem, W. In 2016 24th European Signal Processing Conference (EUSIPCO), pages 728-732, Aug, 2016.
Two distributed algorithms for the deconvolution of large radio-interferometric multispectral images [pdf]Paper  doi  abstract   bibtex   
We address in this paper the deconvolution issue for radio-interferometric multispectral images. Whereas this problem has been widely explored in the recent literature for single images, a few algorithms are able to reconstruct multispectral images (three-dimensional images) [1], [2]. We propose in this paper two new distributed algorithms based on the optimization methods ADMM and projected gradient (PG) for the reconstruction of radio-interferometric multispectral images. We present an original distributed architecture and a comparison of their performance on a quasi-real data cube.
@InProceedings{7760344,
  author = {C. Meillier and P. Bianchi and W. Hachem},
  booktitle = {2016 24th European Signal Processing Conference (EUSIPCO)},
  title = {Two distributed algorithms for the deconvolution of large radio-interferometric multispectral images},
  year = {2016},
  pages = {728-732},
  abstract = {We address in this paper the deconvolution issue for radio-interferometric multispectral images. Whereas this problem has been widely explored in the recent literature for single images, a few algorithms are able to reconstruct multispectral images (three-dimensional images) [1], [2]. We propose in this paper two new distributed algorithms based on the optimization methods ADMM and projected gradient (PG) for the reconstruction of radio-interferometric multispectral images. We present an original distributed architecture and a comparison of their performance on a quasi-real data cube.},
  keywords = {deconvolution;distributed algorithms;gradient methods;image reconstruction;optimization method;alternating direction method of multipliers;projected gradient;image reconstruction;large radio-interferometric multispectral images;image deconvolution;distributed algorithms;ADMM;Signal processing algorithms;Deconvolution;Clustering algorithms;Europe;Signal processing;Minimization;Radio frequency;ADMM;deconvolution;distributed optimization;projected gradient;radio-interferometry;multispectral images},
  doi = {10.1109/EUSIPCO.2016.7760344},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2016/papers/1570255164.pdf},
}
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