Title :
A Complex Contourlet Transform and its HMT model for denoising and texture retrieval
Author :
Wen, Z.J. ; Pu, Z.R. ; Dong Min ; Liu Li
Author_Institution :
Sch. of Inf. Sci. & Eng., Lanzhou Univ., Lanzhou, China
Abstract :
This paper proposes a Complex Contourlet Transform (CCT) and develops the hidden Markov tree (HMT) model for it. Contourlet Transform (CT) has obvious advantages which are multiresolution, locality and multi-directional compared with traditional wavelet and can be considered as an effective tool in capturing geometric structure of natural images. Unfortunately, CT lacks of shift-invariance. The CCT keeps multi-directional of Contourlet and obtains higher shift-invariance by a structure of dual tree Laplacian pyramid (LP). The HMT model for CCT is developed to reveal the statistical dependence and the highly non-Gaussian distribution of the coefficients in subbands among inter- and intra-scales. To test the CCT-HMT model, we apply it on image denoising and texture retrieval. Experiments show that the HMT mode base on CCT achieves better performance compared with the HMT model based on Contourlet, either in denoising or in texture retrieval.
Keywords :
discrete wavelet transforms; hidden Markov models; image denoising; image retrieval; trees (mathematics); CCT-HMT model; complex contourlet transform; dual tree Laplacian pyramid; geometric structure; hidden Markov tree; image denoising; natural images; nonGaussian distribution; shift-invariance; statistical dependence; texture retrieval; HMT model; complex contourlet; denoising; texture retrieval;
Conference_Titel :
Signal Processing (ICSP), 2012 IEEE 11th International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4673-2196-9
DOI :
10.1109/ICoSP.2012.6491710