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Publication - Dr Vladislav Tadic

    Reconstruction of compressively sensed ultrasound RF echoes by exploiting non-Gaussianity and temporal structure

    Citation

    Porter, R, Tadic, V & Achim, A, 2015, ‘Reconstruction of compressively sensed ultrasound RF echoes by exploiting non-Gaussianity and temporal structure’. in: 2015 IEEE International Conference on Image Processing (ICIP). Institute of Electrical and Electronics Engineers (IEEE), pp. 6-10

    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.

    Full details in the University publications repository