@InProceedings{7362893,
  author = {Z. Chen and N. Zhao and A. Basarab and D. Kouamé},
  booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
  title = {Ultrasound compressive deconvolution with ℓP-Norm prior},
  year = {2015},
  pages = {2791-2795},
  abstract = {It has been recently shown that compressive sampling is an interesting perspective for fast ultrasound imaging. This paper addresses the problem of compressive deconvolution for ultrasound imaging systems using an assumption of generalized Gaussian distributed tissue reflectivity function. The benefit of compressive deconvolution is the joint volume reduction of the acquired data and the image resolution improvement. The main contribution of this work is to apply the framework of compressive deconvolution on ultrasound imaging and to propose a novel ℓp-norm (1 ≤ p ≤ 2) algorithm based on Alternating Direction Method of Multipliers. The performance of the proposed algorithm is tested on simulated data and compared with those obtained by a more intuitive sequential compressive deconvolution method.},
  keywords = {biomedical ultrasonics;compressed sensing;deconvolution;Gaussian processes;image resolution;sequential decoding;ultrasonic imaging;ultrasound compressive deconvolution;compressive sampling;ultrasound imaging systems;Gaussian distributed tissue reflectivity function;joint volume reduction;image resolution;alternating direction method of multipliers;sequential compressive deconvolution method;Deconvolution;Image coding;Imaging;Ultrasonic imaging;Radio frequency;Signal processing algorithms;Minimization;ultrasound imaging;compressive sampling;deconvolution;generalized Gaussian distribution;alternating direction method of multipliers},
  doi = {10.1109/EUSIPCO.2015.7362893},
  issn = {2076-1465},
  month = {Aug},
}

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This paper addresses the problem of compressive deconvolution for ultrasound imaging systems using an assumption of generalized Gaussian distributed tissue reflectivity function. The benefit of compressive deconvolution is the joint volume reduction of the acquired data and the image resolution improvement. The main contribution of this work is to apply the framework of compressive deconvolution on ultrasound imaging and to propose a novel ℓp-norm (1 ≤ p ≤ 2) algorithm based on Alternating Direction Method of Multipliers. The performance of the proposed algorithm is tested on simulated data and compared with those obtained by a more intuitive sequential compressive deconvolution method.","keywords":"biomedical ultrasonics;compressed sensing;deconvolution;Gaussian processes;image resolution;sequential decoding;ultrasonic imaging;ultrasound compressive deconvolution;compressive sampling;ultrasound imaging systems;Gaussian distributed tissue reflectivity function;joint volume reduction;image resolution;alternating direction method of multipliers;sequential compressive deconvolution method;Deconvolution;Image coding;Imaging;Ultrasonic imaging;Radio frequency;Signal processing algorithms;Minimization;ultrasound imaging;compressive sampling;deconvolution;generalized Gaussian distribution;alternating direction method of multipliers","doi":"10.1109/EUSIPCO.2015.7362893","issn":"2076-1465","month":"Aug","bibtex":"@InProceedings{7362893,\n author = {Z. Chen and N. Zhao and A. Basarab and D. Kouamé},\n booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},\n title = {Ultrasound compressive deconvolution with ℓP-Norm prior},\n year = {2015},\n pages = {2791-2795},\n abstract = {It has been recently shown that compressive sampling is an interesting perspective for fast ultrasound imaging. This paper addresses the problem of compressive deconvolution for ultrasound imaging systems using an assumption of generalized Gaussian distributed tissue reflectivity function. The benefit of compressive deconvolution is the joint volume reduction of the acquired data and the image resolution improvement. The main contribution of this work is to apply the framework of compressive deconvolution on ultrasound imaging and to propose a novel ℓp-norm (1 ≤ p ≤ 2) algorithm based on Alternating Direction Method of Multipliers. 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