Edge-preserving Bayesian image superresolution based on compound Markov random fields

Authors:
Atsunori Kanemura;Shin-ichi Maeda;Shin Ishii
Affiliations:
Nara Institute of Science and Technology, Nara, Japan;Nara Institute of Science and Technology, Nara, Japan;Nara Institute of Science and Technology, Nara, Japan
Venue:
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Year:
2007

Citing 8
Cited 2

Bayesian interpolation

Neural Computation
A practical Bayesian framework for backpropagation networks

Neural Computation
A view of the EM algorithm that justifies incremental, sparse, and other variants

Learning in graphical models
Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability)

Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability)
Joint MAP registration and high-resolution image estimation using a sequence of undersampled images

IEEE Transactions on Image Processing
Bayesian multichannel image restoration using compound Gauss-Markov random fields

IEEE Transactions on Image Processing
Stochastic methods for joint registration, restoration, and interpolation of multiple undersampled images

IEEE Transactions on Image Processing

Spatially Varying Mixtures Incorporating Line Processes for Image Segmentation

Journal of Mathematical Imaging and Vision
Accurate image registration for MAP image super-resolution

Image Communication

Quantified Score

Hi-index	0.00

Visualization

Abstract

This study deals with image superresolution problems simultaneously with accompanying image registration problems. The goal of superresolution is to generate a high resolution image by integrating low-resolution degraded observed images. We propose a Bayesian approach whose prior is modeled as a compound Gaussian Markov random field (MRF). This approach is advantageous in preserving discontinuity in the original image, in comparison to the existing single-layer Gaussian MRF models. Maximum-marginalized-likelihood estimation of the registration parameters is carried out by a variational EM algorithm where hidden variables are marginalized out and the posterior distribution is approximated by a factorized trial distribution. High resolution image estimates are obtained as by-products of the EM algorithm. Experiments show that our Bayesian approach with two-layer compound models exhibits better performance in terms of mean square error and visual quality than the single-layer model.