Bayesian deconvolution in nuclear spectroscopy using RJMCMC

Gulam-Razul,; Fitzgerald,; Andrieu,

doi:10.1109/icassp.2002.1005991

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Bayesian Inference1

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2015

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“…Together with the well established use of VB inference in BID, Markov Chain Monte Carlo (MCMC) methods are also gaining popularity. MCMC is the most general method used to approximate a posterior distribution, see [43] [44] [45] for details. The model in Eq.…”

Section: Bayesian Inferencementioning

confidence: 99%

Variational Bayesian Blind Image Deconvolution: A review

Ruiz

Zhou

Mateos

et al. 2015

Digital Signal Processing

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In this paper we provide a review of the recent literature on Bayesian Blind Image Deconvolution (BID) methods. We believe that two events have marked the recent history of BID: the predominance of Variational Bayes (VB) inference as a tool to solve BID problems and the increasing interest of the computer vision community in solving BID problems. VB inference in combination with recent image models like the ones based on Super Gaussian (SG) and Scale Mixture of Gaussians (SMG) representations have led to the use of very general and powerful tools to provide clear images from blurry observations. In the provided review emphasis is paid on VB inference and the use of SG and SMG models with coverage of recent advances in sampling methods. We also provide examples of current state of the art BID methods and discuss problems that very likely will mark the near future of BID.

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Section: Bayesian Inferencementioning

confidence: 99%