Sparse Bayesian Learning for non-Gaussian sources

Porter, Robert; Tadic, V.B.; Achim, Alin

doi:10.1016/j.dsp.2015.06.014

Cited by 5 publications

(2 citation statements)

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“…The T-MSBL and T-MSBL-MoG-a algorithms for the MMV model are as described in the work of [15] and [39]. The core idea is to learn the correlation structure between the measurement vectors and compensate for it.…”

Section: A Temporal-multiple Sparse Bayesian Learning and Temporal-mu...mentioning

confidence: 99%

“…We have previously shown that it is possible to improve the reconstruction performance by taking advantage of the non-Gaussianity, temporal and block structure of the ultrasound data [38], [39], building on the work in [34] which was the first to apply the T-MSBL method (compensating for the negative effect of temporal correlation) to the recovery of compressively sensed ultrasound images. The acquisition of medical ultrasound data in a manner suitable for compressed sensing techniques has been examined in other works, e.g.…”

Section: Introductionmentioning

confidence: 99%

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Recovery of compressively sensed ultrasound images with structured Sparse Bayesian Learning

Porter,

Tadic,

Achim

2018

Preprint

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In this paper, we consider the problem of recovering compressively sensed ultrasound images. We build on prior work, and consider a number of existing approaches that we consider to be the state-of-the-art. The methods we consider take advantage of a number of assumptions on the signals including those of temporal and spatial correlation, block structure, prior knowledge of the support, and non-Gaussianity. We conduct a series of intensive tests to quantify the performance of these methods. We find that by altering the parameters of the structured Sparse Bayesian Learning approaches considered, we can significantly improve the objective quality of the reconstructed images. The results we achieve are a significant improvement upon previously proposed reconstruction techniques. In addition, we further show that by careful choice of parameters, we can obtain near-optimal results whilst requiring only a small fraction of the computational time needed for the best reconstruction quality.

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Section: A Temporal-multiple Sparse Bayesian Learning and Temporal-mu...mentioning

confidence: 99%