Title :
≫Contextual Hidden Markov Tree Model Image Denoising Using a New Nonuniform Quincunx Directional Filter Banks
Author :
Tian, Yong ; Wang, Jianing ; Zhang, Jiuwen ; Yida Ma
Author_Institution :
Lanzhou Univ., Lanzhou
Abstract :
Wavelet-based Hidden Markov Tree (HMT) models have been proven to be useful tools for statistical signal and image processing. But wavelet transform lacks directional information, so it is difficult to get better performance in many image processing tasks with complicated texture images. In this paper, we use the nuqDFB to decompose an image into twelve highpass subbands and one lowpass subband, while remaining perfect reconstruction and maximally decimation property. By repeating the same decomposition to the low- pass subband, a quardtree structure of the coefficients is established that can be used in the training of HMT models. Then, we adopt a new method via contexts to exploit the intra-scale clustering property and implement it in the HMT training iteration process. To illustrate the power of this approach, this algorithm is used in image denoising. The results show that it is obviously superior in both vision and Pulse Signal to Noise Ratio (PSNR).
Keywords :
hidden Markov models; image denoising; trees (mathematics); wavelet transforms; HMT training iteration process; contextual hidden Markov tree model; image denoising; image processing; intrascale clustering property; nonuniform quincunx directional filter banks; statistical signal; wavelet transform; wavelet-based hidden Markov tree model; Clustering algorithms; Filter bank; Hidden Markov models; Image denoising; Image processing; Image reconstruction; PSNR; Signal processing; Signal to noise ratio; Wavelet transforms;
Conference_Titel :
Intelligent Information Hiding and Multimedia Signal Processing, 2007. IIHMSP 2007. Third International Conference on
Conference_Location :
Kaohsiung
Print_ISBN :
978-0-7695-2994-1
DOI :
10.1109/IIH-MSP.2007.1