DocumentCode
3448127
Title
Image denoising using multiple wavelet representations and local contextual hidden Markov model
Author
Zhang, Wei ; Wei, Ke-Tai ; Liu, Xi-Mei
Author_Institution
Coll. of Autom. & Electron. Eng., Qingdao Univ. of Sci. & Technol., Qingdao
fYear
2007
fDate
15-18 Dec. 2007
Firstpage
156
Lastpage
161
Abstract
Wavelet-domain local contextual hidden Markov model (LCHMM) can exploit both the local statistics and the intrascale dependencies of wavelet coefficients at a low computational complexity. Multiple wavelet representations have excellent performance in image denoising. In this paper, combining the multiple wavelet representations with the LCHMM and using their advantages in image denoising, we propose a new image denoising algorithm, called M-LCHMM. It is simple and effective. Simulation results show that the proposed M-LCHMM can achieve the state-of-the-art image denoising performance at the low computational complexity.
Keywords
computational complexity; hidden Markov models; image denoising; wavelet transforms; M-LCHMM; computational complexity; image denoising; local contextual hidden Markov model; local statistics; multiple wavelet representations; wavelet coefficients; Computational complexity; Context modeling; Degradation; Discrete wavelet transforms; Hidden Markov models; Image denoising; Noise reduction; Robotics and automation; Statistics; Wavelet coefficients; Image denoising; Local Contextual Hidden Markov Model; multiple wavelet representations;
fLanguage
English
Publisher
ieee
Conference_Titel
Robotics and Biomimetics, 2007. ROBIO 2007. IEEE International Conference on
Conference_Location
Sanya
Print_ISBN
978-1-4244-1761-2
Electronic_ISBN
978-1-4244-1758-2
Type
conf
DOI
10.1109/ROBIO.2007.4522152
Filename
4522152
Link To Document