@InProceedings{ Ch2017.2,
author = {Chen, Zhouye and Basarab, Adrian and Kouam\'e, Denis},
title = "{Enhanced ultrasound image reconstruction using a compressive blind deconvolution approach (regular paper)}",
booktitle = "{IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2017), New Orleans, 05/03/2017-09/03/2017}",
year = {2017},
month = {March},
publisher = {IEEE : Institute of Electrical and Electronics Engineers},
address = {http://www.ieee.org/},
pages = {6245--6249},
language = {anglais},
URL = {https://doi.org/10.1109/ICASSP.2017.7953357 - https://oatao.univ-toulouse.fr/22037/},
note = { (Conf\'erencier invit\'e)},
abstract = {Compressive deconvolution, combining compressive sampling and image deconvolution, represents an interesting possibility to reconstruct enhanced ultrasound images from compressed measurements. The model of compressive
deconvolution includes, in addition to the measurement matrix, a 2D convolution operator carrying the information on the system point spread function which is usually unkown in practice. In this paper, we propose a novel
alternating minimization-based optimization scheme to invert the resulting linear model, to jointly reconstruct enhanced ultrasound images and estimate the point spread function. The performance of the method is evaluated on both
Shepp-Logan phantom and simulated ultrasound data.}
}

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