@InProceedings{7362663,
  author = {M. Jiang and J. N. Girard and J. -. Starck and S. Corbel and C. Tasse},
  booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
  title = {Compressed sensing and radio interferometry},
  year = {2015},
  pages = {1646-1650},
  abstract = {Radio interferometric imaging constitutes a strong ill-posed inverse problem. In addition, the next generation radio telescopes, such as the Low Frequency Array (LOFAR) and the Square Kilometre Array (SKA), come with an additional direction-dependent effects which impacts the image restoration. In the compressed sensing framework, we used the analysis and synthesis formulation of the problem and we solved it using proximal algorithms. A simple version of our method has been implemented within the LOFAR imager and has been validated on simulated and real LOFAR data. It demonstrated its capability to super-resolve radio sources, to provide correct photometry of point sources in a large field of view and image extended emissions with enhanced quality as compared to classical deconvolution methods. One extension of our method is to use the temporal information of the data to build a 2D-1D sparse imager enabling the detection of transient sources.},
  keywords = {compressed sensing;image restoration;interferometry;radiotelescopes;compressed sensing;radio interferometry;radio interferometric imaging;ill-posed inverse problem;low frequency array;square kilometre array;direction-dependent effects;image restoration;synthesis formulation;proximal algorithms;LOFAR imager;LOFAR data;super-resolve radio sources;photometry;image extended emissions;deconvolution methods;temporal information;2D-1D sparse imager;transient sources detection;Transient analysis;Imaging;Image reconstruction;Compressed sensing;Signal processing algorithms;Dictionaries;Minimization;sparsity;compressed sensing;interferom-etry;imaging;transients},
  doi = {10.1109/EUSIPCO.2015.7362663},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570103663.pdf},
}

{"_id":"Zi4dJtcsEKSwuHzsG","bibbaseid":"jiang-girard-starck-corbel-tasse-compressedsensingandradiointerferometry-2015","downloads":0,"creationDate":"2016-01-08T22:33:14.706Z","title":"Compressed sensing and radio interferometry","author_short":["Jiang, M.","Girard, J. N.","Starck, J. -.","Corbel, S.","Tasse, C."],"year":2015,"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Roznn/EUSIPCO/main/eusipco2015url.bib","bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["M."],"propositions":[],"lastnames":["Jiang"],"suffixes":[]},{"firstnames":["J.","N."],"propositions":[],"lastnames":["Girard"],"suffixes":[]},{"firstnames":["J.","-."],"propositions":[],"lastnames":["Starck"],"suffixes":[]},{"firstnames":["S."],"propositions":[],"lastnames":["Corbel"],"suffixes":[]},{"firstnames":["C."],"propositions":[],"lastnames":["Tasse"],"suffixes":[]}],"booktitle":"2015 23rd European Signal Processing Conference (EUSIPCO)","title":"Compressed sensing and radio interferometry","year":"2015","pages":"1646-1650","abstract":"Radio interferometric imaging constitutes a strong ill-posed inverse problem. In addition, the next generation radio telescopes, such as the Low Frequency Array (LOFAR) and the Square Kilometre Array (SKA), come with an additional direction-dependent effects which impacts the image restoration. In the compressed sensing framework, we used the analysis and synthesis formulation of the problem and we solved it using proximal algorithms. A simple version of our method has been implemented within the LOFAR imager and has been validated on simulated and real LOFAR data. It demonstrated its capability to super-resolve radio sources, to provide correct photometry of point sources in a large field of view and image extended emissions with enhanced quality as compared to classical deconvolution methods. One extension of our method is to use the temporal information of the data to build a 2D-1D sparse imager enabling the detection of transient sources.","keywords":"compressed sensing;image restoration;interferometry;radiotelescopes;compressed sensing;radio interferometry;radio interferometric imaging;ill-posed inverse problem;low frequency array;square kilometre array;direction-dependent effects;image restoration;synthesis formulation;proximal algorithms;LOFAR imager;LOFAR data;super-resolve radio sources;photometry;image extended emissions;deconvolution methods;temporal information;2D-1D sparse imager;transient sources detection;Transient analysis;Imaging;Image reconstruction;Compressed sensing;Signal processing algorithms;Dictionaries;Minimization;sparsity;compressed sensing;interferom-etry;imaging;transients","doi":"10.1109/EUSIPCO.2015.7362663","issn":"2076-1465","month":"Aug","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570103663.pdf","bibtex":"@InProceedings{7362663,\n author = {M. Jiang and J. N. Girard and J. -. Starck and S. Corbel and C. Tasse},\n booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},\n title = {Compressed sensing and radio interferometry},\n year = {2015},\n pages = {1646-1650},\n abstract = {Radio interferometric imaging constitutes a strong ill-posed inverse problem. In addition, the next generation radio telescopes, such as the Low Frequency Array (LOFAR) and the Square Kilometre Array (SKA), come with an additional direction-dependent effects which impacts the image restoration. In the compressed sensing framework, we used the analysis and synthesis formulation of the problem and we solved it using proximal algorithms. A simple version of our method has been implemented within the LOFAR imager and has been validated on simulated and real LOFAR data. It demonstrated its capability to super-resolve radio sources, to provide correct photometry of point sources in a large field of view and image extended emissions with enhanced quality as compared to classical deconvolution methods. One extension of our method is to use the temporal information of the data to build a 2D-1D sparse imager enabling the detection of transient sources.},\n keywords = {compressed sensing;image restoration;interferometry;radiotelescopes;compressed sensing;radio interferometry;radio interferometric imaging;ill-posed inverse problem;low frequency array;square kilometre array;direction-dependent effects;image restoration;synthesis formulation;proximal algorithms;LOFAR imager;LOFAR data;super-resolve radio sources;photometry;image extended emissions;deconvolution methods;temporal information;2D-1D sparse imager;transient sources detection;Transient analysis;Imaging;Image reconstruction;Compressed sensing;Signal processing algorithms;Dictionaries;Minimization;sparsity;compressed sensing;interferom-etry;imaging;transients},\n doi = {10.1109/EUSIPCO.2015.7362663},\n issn = {2076-1465},\n month = {Aug},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570103663.pdf},\n}\n\n","author_short":["Jiang, M.","Girard, J. N.","Starck, J. -.","Corbel, S.","Tasse, C."],"key":"7362663","id":"7362663","bibbaseid":"jiang-girard-starck-corbel-tasse-compressedsensingandradiointerferometry-2015","role":"author","urls":{"Paper":"https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570103663.pdf"},"keyword":["compressed sensing;image restoration;interferometry;radiotelescopes;compressed sensing;radio interferometry;radio interferometric imaging;ill-posed inverse problem;low frequency array;square kilometre array;direction-dependent effects;image restoration;synthesis formulation;proximal algorithms;LOFAR imager;LOFAR data;super-resolve radio sources;photometry;image extended emissions;deconvolution methods;temporal information;2D-1D sparse imager;transient sources detection;Transient analysis;Imaging;Image reconstruction;Compressed sensing;Signal processing algorithms;Dictionaries;Minimization;sparsity;compressed sensing;interferom-etry;imaging;transients"],"metadata":{"authorlinks":{}},"downloads":0},"search_terms":["compressed","sensing","radio","interferometry","jiang","girard","starck","corbel","tasse"],"keywords":["compressed sensing;image restoration;interferometry;radiotelescopes;compressed sensing;radio interferometry;radio interferometric imaging;ill-posed inverse problem;low frequency array;square kilometre array;direction-dependent effects;image restoration;synthesis formulation;proximal algorithms;lofar imager;lofar data;super-resolve radio sources;photometry;image extended emissions;deconvolution methods;temporal information;2d-1d sparse imager;transient sources detection;transient analysis;imaging;image reconstruction;compressed sensing;signal processing algorithms;dictionaries;minimization;sparsity;compressed sensing;interferom-etry;imaging;transients"],"authorIDs":[],"dataSources":["eov4vbT6mnAiTpKji","knrZsDjSNHWtA9WNT"]}