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EnglishIn this paper, a new approach based on the Bayesian compressive sampling (BCS ) and within the contrast source formulation of an inverse scattering problem is proposed for imaging sparse scatterers. By enforcing a probabilistic hierarchical prior as a sparsity regularization constraint, the problem is solved by means of a fast relevance vector machine. The effectiveness and robustness of the BCS-based approach are assessed through a set of numerical experiments concerned with various scatterer configurations and different noisy conditions.
| Titolo: | A bayesian compressive sampling-based inversion for imaging sparse scatterers |
| Autori Unitn: | |
| Anno di pubblicazione: | 2011 |
| Titolo del periodico: | |
| Abstract: | In this paper, a new approach based on the Bayesian compressive sampling (BCS ) and within the contrast source formulation of an inverse scattering problem is proposed for imaging sparse scatterers. By enforcing a probabilistic hierarchical prior as a sparsity regularization constraint, the problem is solved by means of a fast relevance vector machine. The effectiveness and robustness of the BCS-based approach are assessed through a set of numerical experiments concerned with various scatterer configurations and different noisy conditions. |
| Handle: | http://hdl.handle.net/11572/89698 |
| Appare nelle tipologie: | 03.1 Articolo su rivista (Journal article) |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| A bayesian compressive sampling-based inversion for imaging sparse scatterers.pdf | Versione editoriale | Tutti i diritti riservati (All rights reserved) | Administrator |
http://hdl.handle.net/11572/89698
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