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Proceedings Paper

An image-noise estimation approach using singular value decomposition
Author(s): Mingfu He; Mingzhe Liu; Chengqiang Zhao; Jianbo Yang; Helen Zhou
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Paper Abstract

This paper proposes a simple and accurate estimation of the additive white Gaussian noise for the noise-contaminated digital images. One can easily estimate the noise level through singular value decomposition (SVD) to the noise-polluted image if an image is deteriorated by the additive white Gaussian noise. As described in the paper, the sum of some specific singular values has the linear relationship with the standard deviation of noise. Based on no correlation between noises, we add known noises upon a noise image. Then noise level is estimated by solving a nonlinear over-determined matrix equation. The proposed algorithm was experimentally tested by the benchmark images and outperforms estimation method of selecting weak textured patches using principal component analysis (PCA). The proposed method is more independent on the original image information and presents a higher accuracy and a stronger robustness for a range of noise level in various images.

Paper Details

Date Published: 29 August 2016
PDF: 6 pages
Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100331V (29 August 2016); doi: 10.1117/12.2243817
Show Author Affiliations
Mingfu He, Chengdu Univ. of Technology (China)
Mingzhe Liu, Chengdu Univ. of Technology (China)
Chengqiang Zhao, Chengdu Univ. of Technology (China)
Jianbo Yang, Chengdu Univ. of Technology (China)
Helen Zhou, Manukau Institute of Technology (New Zealand)

Published in SPIE Proceedings Vol. 10033:
Eighth International Conference on Digital Image Processing (ICDIP 2016)
Charles M. Falco; Xudong Jiang, Editor(s)

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