The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
We present a novel approach to inverse problems in imaging based on a Hierarchical Bayesian-MAP (HB-MAP) formulation. In this paper we specifically focus on the difficult and basic inverse problem of multi-sensor (tomographic) imaging wherein the source image of interest is viewed from multiple directions by independent sensors. We employ a Probabilistic Graphical Modeling extension of the Compound Gaussian (CG) distribution as a global image prior into a Hierarchical Bayesian inference procedure. We first demonstrate the performance of the algorithm on Monte-Carlo trials followed by empirical data involving natural (optical) images. We demonstrate how our algorithm outperforms many of the previous approaches in the literature including Filtered Back-projection (FBP) and a variety of state-of-the-art compressive sensing (CS) algorithms.