Wavelet-based image denoising using a Markov random field a priori model

1997 ◽  
Vol 6 (4) ◽  
pp. 549-565 ◽  
Author(s):  
M. Malfait ◽  
D. Roose
Keyword(s):  
Random Field ◽  
Image Denoising ◽  
Markov Random Field ◽  
A Priori ◽  
Markov Random

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.

scholarly journals MRI Reconstruction Using Markov Random Field and Total Variation as Composite Prior

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3185
Author(s):  
Marko Panić ◽  
Dušan Jakovetić ◽  
Dejan Vukobratović ◽  
Vladimir Crnojević ◽  
Aleksandra Pižurica
Keyword(s):  
Random Field ◽  
Total Variation ◽  
Markov Random Field ◽  
Adaptive Estimation ◽  
A Priori ◽  
Reconstruction Algorithm ◽  
Original Data ◽  
Magnetic Resonance Images ◽  
Reconstruction Method ◽  
Markov Random

Reconstruction of magnetic resonance images (MRI) benefits from incorporating a priori knowledge about statistical dependencies among the representation coefficients. Recent results demonstrate that modeling intraband dependencies with Markov Random Field (MRF) models enable superior reconstructions compared to inter-scale models. In this paper, we develop a novel reconstruction method, which includes a composite prior based on an MRF model and Total Variation (TV). We use an anisotropic MRF model and propose an original data-driven method for the adaptive estimation of its parameters. From a Bayesian perspective, we define a new position-dependent type of regularization and derive a compact reconstruction algorithm with a novel soft-thresholding rule. Experimental results show the effectiveness of this method compared to the state of the art in the field.

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