Continuous-wave THz image denoising based on Markov random field and simulated annealing algorithm

2009 ◽  
Author(s):  
Qi Li ◽  
Qiguo Yin ◽  
Rui Yao ◽  
Qi Wang
Keyword(s):  
Simulated Annealing ◽  
Random Field ◽  
Image Denoising ◽  
Markov Random Field ◽  
Simulated Annealing Algorithm ◽  
Continuous Wave ◽  
Markov Random ◽  
Annealing Algorithm

Image denoising based on hierarchical Markov random field

2011 ◽  
Vol 32 (2) ◽  
pp. 368-374 ◽  
Author(s):  
Yang Cao ◽  
Yupin Luo ◽  
Shiyuan Yang
Keyword(s):  
Random Field ◽  
Image Denoising ◽  
Markov Random Field ◽  
Markov Random

Bayesian Image Denoising Using an Anisotropic Markov Random Field Model

2011 ◽  
Vol 467-469 ◽  
pp. 2018-2023
Author(s):  
Yan Qiu Cui ◽  
Tao Zhang ◽  
Shuang Xu ◽  
Hou Jie Li
Keyword(s):  
Computational Complexity ◽  
Random Field ◽  
Image Denoising ◽  
Markov Random Field ◽  
Field Model ◽  
Wavelet Domain ◽  
Map Estimation ◽  
Denoising Method ◽  
Markov Random ◽  
Restoration Method

This paper presents a Bayesian denoising method based on an anisotropic Markov Random Field (MRF) model in wavelet domain in order to improve the image denoising performance and reduce the computational complexity. The classical single-resolution image restoration method using MRFs and the maximum a posteriori (MAP) estimation is extended to the wavelet domain. To obtain the accurate MAP estimation, a novel anisotropic MRF model is proposed under this framework. As compared to the simple isotropic MRF model, this new model can capture the intrascale dependencies of wavelet coefficients significantly better. Simulation results demonstrate our proposed method has a good denoising performance while reducing the computational complexity.

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