Proceedings ArticleDOI
Contourlet Image Modeling with Contextual Hidden Markov Models
Zhiling Long,N.H. Younan +1 more
- pp 173-177
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TLDR
A contextual hidden Markov model, which was successfully applied to wavelet image denoising, has been adapted into the contourlet domain and the resulting contourlets contextual HMM has been tested in a Denoising application with promising results, which verified its effectiveness in characterizing contourlett images.Citations
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Journal ArticleDOI
Fast communication: Statistical image modeling in the contourlet domain using contextual hidden Markov models
Zhiling Long,Nicolas H. Younan +1 more
TL;DR: This new model is demonstrated to be a better model for contourlet images than the state of the art contourlets hidden Markov tree model and shows more potential than the baseline W-CHMM.
Journal ArticleDOI
A Comparative Study in Wavelets, Curvelets and Contourlets as Denoising Biomedical Images
TL;DR: It has been found that contourlets transform outperforms the curvelets and wavelet transform in terms of signal noise ratio.
Journal ArticleDOI
Multiscale texture segmentation via a contourlet contextual hidden Markov model
Zhiling Long,Nicolas H. Younan +1 more
TL;DR: This paper develops a multiscale texture segmentation technique based on the C-CHMM that provides improved accuracy in segmenting texture patterns of diversified nature, as compared with popular methods such as the HMTseg and the JMCMS.
Proceedings ArticleDOI
Statistical Modeling of Multi-modal Medical Image Fusion Method Using C-CHMM and M-PCNN
TL;DR: A new Contextual hidden Markov Model and modified Pulse Coupled Neural Network based fusion approach in the Contour domain is proposed for multi-modal medical image fusion and the experimental results demonstrate that the presented fusion method can further improve fusion image quality and visual effects.
Journal ArticleDOI
A Comparative Study in Wavelets, Curvelets and Contourlets as Denoising biomedical Images
TL;DR: It has been found that the contourlets transform outperforms the curvelets and wavelet transform in terms of signal noise ratio.
References
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Journal ArticleDOI
Wavelet-based statistical signal processing using hidden Markov models
TL;DR: A new framework for statistical signal processing based on wavelet-domain hidden Markov models (HMMs) that concisely models the statistical dependencies and non-Gaussian statistics encountered in real-world signals is developed.
Journal ArticleDOI
Directional multiscale modeling of images using the contourlet transform
D.D.-Y. Po,Minh N. Do +1 more
TL;DR: This study reveals the highly non-Gaussian marginal statistics and strong interlocation, interscale, and interdirection dependencies of contourlet coefficients and finds that conditioned on the magnitudes of their generalized neighborhood coefficients, contours coefficients can be approximately modeled as Gaussian random variables.
Proceedings ArticleDOI
Contourlets: a directional multiresolution image representation
Minh N. Do,Martin Vetterli +1 more
TL;DR: The contourlet transform can be designed to satisfy the anisotropy scaling relation for curves, and thus offers a fast and structured curvelet-like decomposition, and provides a sparse representation for two-dimensional piecewise smooth signals resembling images.
Journal ArticleDOI
Rotation invariant texture characterization and retrieval using steerable wavelet-domain hidden Markov models
Minh N. Do,Martin Vetterli +1 more
TL;DR: A statistical model for characterizing texture images based on wavelet-domain hidden Markov models that can be easily steered to characterize that texture at any other orientation and obtains a rotation-invariant model of the texture image.
Journal ArticleDOI
Wavelet-based texture analysis and synthesis using hidden Markov models
Guoliang Fan,Xiang-Gen Xia +1 more
TL;DR: Wang et al. as mentioned in this paper developed a new hidden Markov tree (HMT) for statistical texture characterization in the wavelet domain, where the joint statistics captured by HMT can also exploit the cross correlation across DWT subbands.