In this paper, a contrast source compressive sensing method for three-dimensional imaging is presented. A fast Relevance Vector Machine (RVM) is used to find the best hyperparameter vector maximizing the cost function of the problem formulated in a multi-task Bayesian Compressive Sensing (MT-BCS) from which the corresponding equivalent currents and the contrast function of the imaged domain are derived. A comparative assessment is reported in order to show the effectiveness of the proposed methodology.
Three Dimensional Imaging with the Contrast Source Compressive Sampling / Anselmi, N.; Poli, L.; Oliveri, G.; Massa, A.. - STAMPA. - (2018), pp. 1-2. (Intervento presentato al convegno ACES-China 2018 tenutosi a Beijing, China nel 29th July-1st August 2018) [10.23919/ACESS.2018.8669353].
Three Dimensional Imaging with the Contrast Source Compressive Sampling
Anselmi N.;Poli L.;Oliveri G.;Massa A.
2018-01-01
Abstract
In this paper, a contrast source compressive sensing method for three-dimensional imaging is presented. A fast Relevance Vector Machine (RVM) is used to find the best hyperparameter vector maximizing the cost function of the problem formulated in a multi-task Bayesian Compressive Sensing (MT-BCS) from which the corresponding equivalent currents and the contrast function of the imaged domain are derived. A comparative assessment is reported in order to show the effectiveness of the proposed methodology.| File | Dimensione | Formato | |
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