Proceedings of the 2016 6th International Conference on Management, Education, Information and Control (MEICI 2016)

Research on the New Image De-noising Methodology Based on Neural Network and HMM-Hidden Markov Models

Authors
Wenzhun Huang, Xinxin Xie
Corresponding Author
Wenzhun Huang
Available Online September 2016.
DOI
10.2991/meici-16.2016.97How to use a DOI?
Keywords
De-noising; Methodology; Neural network; HMM; Markov models
Abstract

In this paper, we conduct research on the new image de-noising methodology based on the neural network and HMM-hidden Markov models. HMM is a double stochastic process, one of which is a Markov chain, this is basic random process as it describes the state of the shift and another random process description statistics corresponding relations between the state and the observation. We apply the method into the image de-noising application that will enhance the general performance with the better accuracy. In the future, we will integrate the simulation steps to verify the effectiveness.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 6th International Conference on Management, Education, Information and Control (MEICI 2016)
Series
Advances in Intelligent Systems Research
Publication Date
September 2016
ISBN
978-94-6252-251-0
ISSN
1951-6851
DOI
10.2991/meici-16.2016.97How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Wenzhun Huang
AU  - Xinxin Xie
PY  - 2016/09
DA  - 2016/09
TI  - Research on the New Image De-noising Methodology Based on Neural Network and HMM-Hidden Markov Models
BT  - Proceedings of the 2016 6th International Conference on Management, Education, Information and Control (MEICI 2016)
PB  - Atlantis Press
SP  - 466
EP  - 470
SN  - 1951-6851
UR  - https://doi.org/10.2991/meici-16.2016.97
DO  - 10.2991/meici-16.2016.97
ID  - Huang2016/09
ER  -