Monte Carlo Markov chain techniques for unsupervised MRF-based image denoising

Author:

Tonazzini A.,Bedini L.

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference18 articles.

1. Models and algorithms for edge-preserving image reconstruction;Bedini,1996

2. Image restoration preserving discontinuities: the Bayesian approach and neural networks;Bedini;Image Vision Comput.,1992

3. On the statistical analysis of dirty pictures;Besag;J. Royal Statist. Soc. B,1986

4. Visual Reconstruction;Blake,1987

5. Maximum likelihood estimation of Markov random field parameters using Markov Chain Monte Carlo algorithms;Descombes,1997

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Maximum a Posteriori Image Denoising with Edge-Preserving Markov Random Field Regularization;Applied Mechanics and Materials;2013-10

2. The Research of Capacitance Characteristics of MRF;Applied Mechanics and Materials;2012-12

3. Image recovery by removing stochastic artefacts identified as local asymmetries;Journal of Instrumentation;2012-04-23

4. Image denoising based on hierarchical Markov random field;Pattern Recognition Letters;2011-01

5. Finite-element-model Updating Using a Bayesian Approach;Finite-element-model Updating Using Computional Intelligence Techniques;2010

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