Abstract
In this paper, we propose a method to solve a compressed sensing problem in the multiple measurement vector model using a mixture of Gaussians prior, inspired by existing sparse Bayesian learning approaches. We show that in the multiple measurement vector model we can take advantage of having multiple samples to learn the properties of the distributions of the sources as part of the reconstruction process, and we show that this method can be applied to significantly improve the reconstruction quality of ultrasound images. We further show that we can also improve the quality of reconstruction by taking advantage of the block structure of ultrasound images, using an existing algorithm for block sparse Bayesian learning.
Original language | English |
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Title of host publication | 2015 IEEE International Conference on Image Processing (ICIP) |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 6-10 |
Number of pages | 5 |
Volume | 2015-December |
ISBN (Print) | 9781479983391 |
DOIs | |
Publication status | Published - 9 Dec 2015 |
Event | 2015 IEEE International Conference on Image Processing (ICIP) - Quebec City, ON, Canada Duration: 27 Sep 2015 → 30 Sep 2015 |
Conference
Conference | 2015 IEEE International Conference on Image Processing (ICIP) |
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Country/Territory | Canada |
City | Quebec City, ON |
Period | 27/09/15 → 30/09/15 |
Keywords
- compressed sensing
- non Gaussian
- ultrasound