|Wavelet-domain HMT-based image superresolution|
|Zhao Shubin; Han Hua; Peng Silong; Shubin Zhao
|Conference Name||Proceedings: 2003 International Conference on Image Processing ICIP-2003
|Conference Place||SpainBarcelona Spain
|Abstract||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 Cyclespinning 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.|
|Keyword||Hidden Markov Tree Wavelets Image Superresolution
|Corresponding Author||Shubin Zhao|
Zhao Shubin,Han Hua,Peng Silong,et al. Wavelet-domain HMT-based image superresolution[C],2003:pp 953-956.
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