Wavelet-domain HMT-based image super-resolution

In this paper we propose an image super-resolution algorithm using wavelet-domain hidden Markov tree (HMT) model. Wavelet-domain HMT models the dependencies of multiscale wavelet coefficients through the state probabilities of wavelet coefficients, whose distribution densities can be approximated by...

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Bibliographic Details
Main Authors: Shubin Zhao, Hua Han, Silong Peng
Format: Conference Proceeding
Language:English
Subjects:
Constraint optimization
Degradation
Discrete wavelet transforms
Hidden Markov models
Image resolution
Probability
Random variables
Spinning
Wavelet coefficients
Wavelet domain
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Description
Summary:In this paper we propose an image super-resolution algorithm using wavelet-domain hidden Markov tree (HMT) model. Wavelet-domain HMT models the dependencies of multiscale wavelet coefficients through the state probabilities of wavelet coefficients, whose distribution densities can be approximated by the Gaussian mixture. Because wavelet-domain HMT accurately characterizes the statistics of real-world images, we reasonably specify it as the prior distribution and then formulate the image super-resolution problem as a constrained optimization problem. And the cycle-spinning technique is used to suppress the artifacts that may exist in the reconstructed high-resolution images. Quantitative error analyses are provided and several experimental images are shown for subjective assessment.
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2003.1246841