Image noise estimation method based on sparse principal component analysis

Hua YANG

doi:10.3788/YJYXS20193409.0913

您当前的位置：

首页 >

文章列表页 >

Image noise estimation method based on sparse principal component analysis

Image Processing | 更新时间：2020-07-05

- Image noise estimation method based on sparse principal component analysis
- Chinese Journal of Liquid Crystals and Displays Vol. 34, Issue 9, Pages: 913-920(2019)
- 作者机构：
  
  南充职业技术学院电子信息工程系, 四川南充 637000
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20193409.0913
  CLC： TP394.1;TH691.9
- Received：28 June 2019，
  
  Accepted：24 July 2019，
  
  Published：05 September 2019
- 稿件说明：
移动端阅览
Hua YANG. Image noise estimation method based on sparse principal component analysis[J]. Chinese journal of liquid crystals and displays, 2019, 34(9): 913-920.
DOI：

Hua YANG. Image noise estimation method based on sparse principal component analysis[J]. Chinese journal of liquid crystals and displays, 2019, 34(9): 913-920. DOI： 10.3788/YJYXS20193409.0913.

摘要

实现数字图像中噪声参数的精确估计对提高图像处理的质量有重要意义。对被高斯白噪声所污染的图像进行稀疏主成分分析时，其部分主成分的负载向量的均值与高斯白噪声标准差呈现一定的线性关系。基于此特征，本文提出了一种快速精确的图像噪声估计方法。在该方法中，通过对高斯白噪声污染图像添加多种已知标准差等级的新的高斯白噪声以产生多幅新图像，然后对每幅图像进行稀疏主成分分析，并求取多个主成分负载向量均值。最后，通过求解一个超定方程组实现图像高斯噪声标准差等级的精确估计。实验结果表明，本方法在低噪声（

=5）到高噪声（

=70）条件下均具有较高的估计精度和较强的鲁棒性，在实际工程中具有一定的实用价值。

Abstract

Realizing accurate estimation of noise parameters in digital images is of great significance for improving the quality of image processing. When the sparse principal component analysis is performed on the image contaminated by Gaussian white noise

the mean value of the load vector of some principal components and the standard deviation of Gaussian white noise show a certain linear relationship. Based on this feature

this paper proposes a fast and accurate image noise estimation method. In this method

a plurality of new Gaussian white noises of known standard deviation levels are added to the Gaussian white noise-contaminated image to generate a plurality of new images

and then each sample is subjected to sparse principal component analysis and mean of multiple principal component load vectors are obtained. Finally

an accurate estimation of the standard deviation of Gaussian noise is achieved by solving an over determined system of equations. The experiment results show that the method has high estimation accuracy under low noise (

=5) to high noise (

=70) conditions

and strong robustness is demonstrated. This method has certain practical value in practical engineering.

关键词

Keywords

references

KIMLYK M, UMNYASHKIN S. Image denoising using discrete wavelet transform and edge information[C]// Proceedings of 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering . Moscow: IEEE, 2018: 1823-1825.

XU J, ZHANG L, ZHANG D.External prior guided internal prior learning for real-world noisy image denoising[J]. IEEE Transactions on Image Processing , 2018, 27(6): 2996-3010.

CHANDNI C S, PUSHPAKUMARI R. Reduced hardware architecture of bilateral filter for real time image denoising[C]// Proceedings of 2017 International Conference on Intelligent Computing , Instrumentation and Control Technologies . Kannur: IEEE, 2017: 769-774.

WANG Z, HOU G J, PAN Z K, et al . Single image dehazing and denoising combining dark channel prior and variational models[J]. IET Computer Vision , 2018, 12(4): 393-402.

YUAN H Z, JIA J Z, ZHU Z X. SIPID: a deep learning framework for sinogram interpolation and image denoising in low-dose CT reconstruction[C]// Proceedings of 2018 IEEE 15 th International Symposium on Biomedical Imaging . Washington: IEEE, 2018: 1521-1524.

郭丙华, 岑志松.小波去噪和神经网络相融合的超分辨率图像重建[J].激光杂志, 2016, 37(2):61-64.

GUO B H, CEN Z S. Super resolution image reconstruction based on wavelet denoising and neural network[J]. Laser Journal , 2016, 37(2): 61-64. (in Chinese)

汪浩然, 夏克文, 牛文佳, 等.结合分块噪声估计的字典学习图像去噪算法[J].计算机应用研究, 2017, 34(10):3153-3156, 3161.

WANG H R, XIA K W, NIU W J, et al . Image denoising algorithm combined with dictionary learning and blocked-based noise estimation[J]. Application Research of Computers , 2017, 34(10): 3153-3156, 3161. (in Chinese)

PYATYKH S, HESSER J, ZHENG L. Image noise level estimation by principal component analysis[J]. IEEE Transactions on Image Processing , 2013, 22(2): 687-699.

FAHMY M F, FAHMY O M. An enhanced denoising technique using dual tree complex wavelet transform[C]// Proceedings of 2016 33 rd National Radio Science Conference . Aswan, Egypt: IEEE, 2016: 205-211.

BARBU T. Variational image denoising approach with diffusion porous media flow[J]. Abstract and Applied Analysis , 2013, 2013: 856876.

ZORAN D, WEISS Y. Scale invariance and noise in natural images[C]// Proceedings of 2009 IEEE 12 th International Conference on Computer Vision . Kyoto, Japan: IEEE, 2009: 2209-2216.

BRO R, SMILDE A K. Principal component analysis[J]. Analytical Methods , 2014, 6(9): 2812-2831.

LEON S J.线性代数[M].张文博, 张丽静, 译.9版.北京: 机械工业出版社, 2015.

LEON S J. Linear Algebra with Applications [M]. ZHANG W B, ZHANG L J, trans. 9th ed. Beijing: Mechanical Industry Press, 2015. (in Chinese)

ZOU H, HASTIE T, TIBSHIRANI R. Sparse principal component analysis[J]. Journal of Computational and Graphical Statistics , 2006, 15(2): 265-286.

LIU X H, MASAYUKI T, MASATOSHI O. Noise level estimation using weak textured patches of a single noisy image[C]// Proceedings of 2012 IEEE 19 th International Conference on Image Processing . Orlando, FL, USA: IEEE, 2012: 665-668.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China 130033 Postal code：130033
Tel：0431-86176059 Email：yjxs@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰