Semi-blind ultrasound image deconvolution from compressed measurements. Chen, Z., Basarab, A., & Kouamé, D. Ingénierie et Recherche BioMédicale, 39(1):26–34, Elsevier Masson, http://elsevier-masson.fr, février, 2018. ![Semi-blind ultrasound image deconvolution from compressed measurements [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex The recently proposed framework of ultrasound compressive deconvolution offers the possibility of decreasing the acquired data while improving the image spatial resolution. By combining compressive sampling and image deconvolution, the direct model of compressive deconvolution combines random projections and 2D convolution with a spatially invariant point spread function. Considering the point spread function known, existing algorithms have shown the ability of this framework to reconstruct enhanced ultrasound images from compressed measurements by inverting the forward linear model. In this paper, we propose an extension of the previous approach for compressive blind deconvolution, whose aim is to jointly estimate the ultrasound image and the system point spread function. The performance of the method is evaluated on both simulated and in vivo ultrasound data.
@Article{ ChBaKo2018.1,
author = {Chen, Zhouye and Basarab, Adrian and Kouam\'e, Denis},
title = "{Semi-blind ultrasound image deconvolution from compressed measurements}",
journal = {Ing\'enierie et Recherche BioM\'edicale},
publisher = {Elsevier Masson},
address = {http://elsevier-masson.fr},
year = {2018},
month = {f\'evrier},
volume = {39},
number = {1},
pages = {26--34},
language = {anglais},
URL = {https://doi.org/10.1016/j.irbm.2017.11.002 - https://oatao.univ-toulouse.fr/24697/},
abstract = {The recently proposed framework of ultrasound compressive deconvolution offers the possibility of decreasing the acquired data while improving the image spatial resolution. By combining compressive sampling and image
deconvolution, the direct model of compressive deconvolution combines random projections and 2D convolution with a spatially invariant point spread function. Considering the point spread function known, existing algorithms have
shown the ability of this framework to reconstruct enhanced ultrasound images from compressed measurements by inverting the forward linear model. In this paper, we propose an extension of the previous approach for compressive
blind deconvolution, whose aim is to jointly estimate the ultrasound image and the system point spread function. The performance of the method is evaluated on both simulated and in vivo ultrasound data.}
}
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By combining compressive sampling and image deconvolution, the direct model of compressive deconvolution combines random projections and 2D convolution with a spatially invariant point spread function. Considering the point spread function known, existing algorithms have shown the ability of this framework to reconstruct enhanced ultrasound images from compressed measurements by inverting the forward linear model. In this paper, we propose an extension of the previous approach for compressive blind deconvolution, whose aim is to jointly estimate the ultrasound image and the system point spread function. 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By combining compressive sampling and image\ndeconvolution, the direct model of compressive deconvolution combines random projections and 2D convolution with a spatially invariant point spread function. Considering the point spread function known, existing algorithms have\nshown the ability of this framework to reconstruct enhanced ultrasound images from compressed measurements by inverting the forward linear model. In this paper, we propose an extension of the previous approach for compressive\nblind deconvolution, whose aim is to jointly estimate the ultrasound image and the system point spread function. 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