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
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;
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
DOI :
10.1109/ROBIO.2007.4522152