Noise Variance Estimation Method Based on Regression Analysis and Principal Component Analysis

WU Jiang; YOU Fei; JIANG Ping

doi:10.11999/JEIT170624

Volume 40 Issue 5

May 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(5): 1195-1201

WU Jiang, YOU Fei, JIANG Ping. Noise Variance Estimation Method Based on Regression Analysis and Principal Component Analysis[J]. Journal of Electronics & Information Technology, 2018, 40(5): 1195-1201. doi: 10.11999/JEIT170624

Citation:

WU Jiang, YOU Fei, JIANG Ping. Noise Variance Estimation Method Based on Regression Analysis and Principal Component Analysis[J]. Journal of Electronics & Information Technology, 2018, 40(5): 1195-1201. doi: 10.11999/JEIT170624

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Noise Variance Estimation Method Based on Regression Analysis and Principal Component Analysis

doi: 10.11999/JEIT170624

(College of Information Engineering, Yulin University, Yulin 719000, China)

Funds:

The National Natural Science Foundation of China (11641002), The Science and Technology Program of Yulin (Gy13-12), The Program of Education Commission of Shaanxi Province (11JK0636)

Received Date: 2017-06-28
Rev Recd Date: 2017-11-24
Publish Date: 2018-05-19

Abstract

Abstract

Accurate and reliable blind noise estimation is an important research topic of digital image processing. The main challenge is how to extract pure noise information for estimating. In recent years, many algorithms use principal component analysis technology to exclude the interference of image textures information, and estimate noise level by using the minimal eigenvalue. So that, the image textures have smallest effect on the minimal eigenvalue, thus this kind of methods performs well for high frequency image (image with abundant textures). The minimal eigenvalue is actually smaller than the true noise variance because of limited image blocks, and the bias is the bigger if the number of image patches is the smaller. If the noise level is estimated as the smallest eigenvalue, the final result will be underestimated. It is found that the relation between the ratio of estimated result to real noise variance and the number of image blocks is power function by using regression analysis, thus the true noise level can be computed by using the minimal eigenvalue and the power function. The experiment results show that the proposed algorithm works well over a large range of visual content and noise conditions, and can process multiply Gaussian noise too.
- Noise image,
- Gaussian noise,
- Noise estimation,
- Principal component analysis,
- Regression analysis

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References(21)

References

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