CASIA OpenIR  > 智能制造技术与系统研究中心  > 多维数据分析
Wavelet-domain HMT-based image superresolution
Zhao Shubin; Han Hua; Peng Silong; Shubin Zhao
2003
Conference NameProceedings: 2003 International Conference on Image Processing ICIP-2003
Pagespp 953-956
Conference Date2003/9/14-2003/9/17
Conference PlaceSpainBarcelona Spain
AbstractIn 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.
KeywordHidden Markov Tree Wavelets Image Superresolution
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12904
Collection智能制造技术与系统研究中心_多维数据分析
Corresponding AuthorShubin Zhao
Recommended Citation
GB/T 7714
Zhao Shubin,Han Hua,Peng Silong,et al. Wavelet-domain HMT-based image superresolution[C],2003:pp 953-956.
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