Enhanced ultrasound image reconstruction using a compressive blind deconvolution approach
Zhouye Chen, Adrian Basarab, Denis Kouame
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.
BibTeX
@inproceedings{icassp2017_enhancedultrasou,
title = {Enhanced ultrasound image reconstruction using a compressive blind deconvolution approach},
author = {Zhouye Chen and Adrian Basarab and Denis Kouame},
booktitle = {ICASSP 2017},
year = {2017}
}