Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Neural Computation
An Introduction to Variational Methods for Graphical Models
Machine Learning
An MRF-Based Approach to Generation of Super-Resolution Images from Blurred Observations
Journal of Mathematical Imaging and Vision
Bayesian parameter estimation via variational methods
Statistics and Computing
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
A variational approach for Bayesian blind image deconvolution
IEEE Transactions on Signal Processing
Compound Gauss-Markov random fields for image estimation
IEEE Transactions on Signal Processing
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction of high-resolution frames from video sequences
IEEE Transactions on Image Processing
Joint MAP registration and high-resolution image estimation using a sequence of undersampled images
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Bayesian multichannel image restoration using compound Gauss-Markov random fields
IEEE Transactions on Image Processing
Parameter estimation in Bayesian high-resolution image reconstruction with multisensors
IEEE Transactions on Image Processing
Fast and robust multiframe super resolution
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Blind Deconvolution Using a Variational Approach to Parameter, Image, and Blur Estimation
IEEE Transactions on Image Processing
Bayesian blind deconvolution from differently exposed image pairs
IEEE Transactions on Image Processing
Bayesian combination of sparse and non-sparse priors in image super resolution
Digital Signal Processing
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This study deals with a reconstruction-type superresolution problem and the accompanying image registration problem simultaneously. We propose a Bayesian approach in which the prior is modeled as a compound Gaussian Markov random field (MRF) and marginalization is performed over unknown variables to avoid overfitting. Our algorithm not only avoids overfitting, but also preserves discontinuity in the estimated image, unlike existing single-layer Gaussian MRF models for Bayesian superresolution. Maximum-marginal-likelihood estimation of the registration parameters is carried out using a variational EM algorithm where hidden variables are marginalized out, and the posterior distribution is variationally approximated by a factorized trial distribution. High-resolution image estimates are obtained through the process of posterior computation in the EM algorithm. Experiments show that our Bayesian approach with the two-layer compound model exhibits better performance both in quantitative measures and visual quality than the single-layer model.