Fundamentals of digital image processing
Fundamentals of digital image processing
Mathematical methods in image reconstruction
Mathematical methods in image reconstruction
Simulation-based approach to estimation of latent variable models
Computational Statistics & Data Analysis
Deblurring subject to nonnegativity constraints
IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
IEEE Transactions on Image Processing
A generalized Gaussian image model for edge-preserving MAP estimation
IEEE Transactions on Image Processing
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Many practical problems involve density estimation from indirect observations and they are classified as indirect density estimation problems. For example, image deblurring and image reconstruction in emission tomography belong to this class. In this paper we propose an iterative approach to solve these problems. This approach has been successfully applied to emission tomography (Ma, 2008). The popular EM algorithm can also be used for indirect density estimation, but it requires that observations follow Poisson distributions. Our method does not involve such assumptions; rather, it is established simply from the Bayes conditional probability model and is termed the Iterative Bayes (IB) algorithm. Under certain regularity conditions, this algorithm converges to the positively constrained solution minimizing the Kullback-Leibler distance, an asymmetric measure involving both logarithmic and linear scales of dissimilarities between two probability distributions.