Image Noise Level Estimation by Neural Networks

DOI

10.2991/meita-15.2015.126How to use a DOI?

Keywords

Noise Estimation; Singular Value Decomposition; Neural Networks; Gaussian noise; Hybrid noise.

Abstract

Aiming at the problem of image noise level estimation, this paper proposes an algorithm for noise estimation by singular value decomposition and neural network. The larger (head) parts of the singular values of an image are mainly affected by main structure of the image, and the rest (tail) parts of the singular values are affected by the intensity of noise. With the increase of noise level, corresponding tail parts of singular values are increased. So, singular values should be good characteristics for noise intensity estimation. Firstly, we add different noise with known intensity on a batch of noise free images, and then select a certain number of fixed size image blocks which standard deviation are minimum from these noisy images. Then singular values of these blocks were fed as the input of the neural network, their corresponding noise standard deviation as the output to train neural network. Finally, in the estimation phase, singular values of noise image were used fed into the trained network to predict the unknown noise intensity. The experimental results show that proposed algorithm is quite promising. It can estimates different types of noise with fast speed and high precise, including Gauss white noise and Hybrid noise.

Copyright

© 2015, 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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TY  - CONF
AU  - Zhiming Wang
AU  - Guobin Yuan
PY  - 2015/08
DA  - 2015/08
TI  - Image Noise Level Estimation by Neural Networks
BT  - Proceedings of the 2015 International Conference on Materials Engineering and Information Technology Applications
PB  - Atlantis Press
SP  - 692
EP  - 697
SN  - 2352-5401
UR  - https://doi.org/10.2991/meita-15.2015.126
DO  - 10.2991/meita-15.2015.126
ID  - Wang2015/08
ER  -