A Complex Contourlet Transform and Its HMT Model for Denoising and Texture Retrieval

摘要：

　　This paper proposes a Complex Contoudet Transform (CCT) and develops the hidden Markovtree (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 orin texture retrieval.

关键词： complex contourlet HMT model denoising texture retrieval

作者: Zhang J.Wen Zhang R.Pu Dong Min Liu Li

作者单位: School of Information Science & Engineering, Lanzhou University, Lanzhou, 730000, P.R.China

会议类型: 国际会议

会议名称: 2012 IEEE 11th International Conference on Signal Processing (第11届IEEE信号处理国际会议)

会议地点: 北京

会议语种:英文

页码: 833-837

在线出版日期: 2012-10-21（万方平台首次上网日期，不代表论文的发表时间）