@InProceedings{7760310,
  author = {J. Kim and A. Basarab and P. R. Hill and D. R. Bull and D. Kouamé and A. Achim},
  booktitle = {2016 24th European Signal Processing Conference (EUSIPCO)},
  title = {Ultrasound image reconstruction from compressed measurements using approximate message passing},
  year = {2016},
  pages = {557-561},
  abstract = {In this paper we propose a novel framework for compressive sampling reconstruction of biomedical ultrasonic images based on the Approximate Message Passing (AMP) algorithm. AMP is an iterative algorithm that performs image reconstruction through image denoising within a compressive sampling framework. In this work, our aim is to evaluate the merits of several combinations of a denoiser and a transform domain, which are the two main factors that determine the recovery performance. In particular, we investigate reconstruction performance in the spatial, DCT, and wavelet domains. We compare the results with existing reconstruction algorithms already used in ultrasound imaging and quantify the performance improvement.},
  keywords = {biomedical ultrasonics;compressed sensing;discrete cosine transforms;image denoising;image reconstruction;iterative methods;medical image processing;message passing;wavelet transforms;compressive sampling reconstruction;biomedical ultrasonic images;approximate message passing algorithm;AMP algorithm;iterative algorithm;image reconstruction;image denoising;transform domain;spatial domains;DCT domains;wavelet domains;Image reconstruction;Discrete cosine transforms;Signal processing algorithms;Ultrasonic imaging;Image coding;Wavelet domain;ultrasonic images;Compressive Sampling;nonconvex optimization;IRLS;AMP;image denoising},
  doi = {10.1109/EUSIPCO.2016.7760310},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2016/papers/1570256168.pdf},
}

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R.","Kouamé, D.","Achim, A."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["J."],"propositions":[],"lastnames":["Kim"],"suffixes":[]},{"firstnames":["A."],"propositions":[],"lastnames":["Basarab"],"suffixes":[]},{"firstnames":["P.","R."],"propositions":[],"lastnames":["Hill"],"suffixes":[]},{"firstnames":["D.","R."],"propositions":[],"lastnames":["Bull"],"suffixes":[]},{"firstnames":["D."],"propositions":[],"lastnames":["Kouamé"],"suffixes":[]},{"firstnames":["A."],"propositions":[],"lastnames":["Achim"],"suffixes":[]}],"booktitle":"2016 24th European Signal Processing Conference (EUSIPCO)","title":"Ultrasound image reconstruction from compressed measurements using approximate message passing","year":"2016","pages":"557-561","abstract":"In this paper we propose a novel framework for compressive sampling reconstruction of biomedical ultrasonic images based on the Approximate Message Passing (AMP) algorithm. 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Basarab and P. R. Hill and D. R. Bull and D. Kouamé and A. Achim},\n booktitle = {2016 24th European Signal Processing Conference (EUSIPCO)},\n title = {Ultrasound image reconstruction from compressed measurements using approximate message passing},\n year = {2016},\n pages = {557-561},\n abstract = {In this paper we propose a novel framework for compressive sampling reconstruction of biomedical ultrasonic images based on the Approximate Message Passing (AMP) algorithm. AMP is an iterative algorithm that performs image reconstruction through image denoising within a compressive sampling framework. In this work, our aim is to evaluate the merits of several combinations of a denoiser and a transform domain, which are the two main factors that determine the recovery performance. In particular, we investigate reconstruction performance in the spatial, DCT, and wavelet domains. 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