Noise reduction of independent component analysis based on NLmeans noise prediction

Jing-yang SUN; Chun-yu YU; Shi-jia DONG

doi:10.3788/OPE.20182602.0511

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Noise reduction of independent component analysis based on NLmeans noise prediction

Information Sciences | 更新时间：2020-07-05

- Noise reduction of independent component analysis based on NLmeans noise prediction
- Optics and Precision Engineering Vol. 26, Issue 2, Pages: 511-516(2018)
- 作者机构：
  
  南京邮电大学光电工程学院, 江苏南京 210023
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20182602.0511
  CLC： TP391.41
- Received：26 June 2017，
  
  Accepted：05 September 2017，
  
  Published：25 February 2018
- 稿件说明：
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Jing-yang SUN, Chun-yu YU, Shi-jia DONG. Noise reduction of independent component analysis based on NLmeans noise prediction[J]. Optics and precision engineering, 2018, 26(2): 511-516.
DOI：

Jing-yang SUN, Chun-yu YU, Shi-jia DONG. Noise reduction of independent component analysis based on NLmeans noise prediction[J]. Optics and precision engineering, 2018, 26(2): 511-516. DOI： 10.3788/OPE.20182602.0511.

摘要

为解决采用独立成分分析算法进行图像降噪需要多个观测信号的问题，提出一种对单张图像冗余信息进行稀疏以生成多个观测信号的方法。该方法首先采用字典压缩算法对原噪声图像稀疏；再采用非局部均值算法对压缩图像的冗余信息进行处理，将处理后的冗余信息生成初次降噪图像；将初次降噪图像和原噪声图像共同作为独立成分分析的多个观测信号。结合非局部均值算法可以避免仅使用字典压缩算法造成的过量稀疏，研究表明当高斯白噪声标准差σ在20~45范围时，本文提出的方法比字典稀疏压缩算法和非局部均值算法降噪效果更好，图像降噪后的峰值信噪比是降噪前的1.4倍。本文提出的方法在高斯白噪声标准差σ在20~45范围时，具有很好的降噪效果。

Abstract

It is well known that multiple observed signals are required for image denoising with ICA(Independent Component Analysis). In this paper

a method that multiple observations were generated by making the reduntant imformation of a single image sparse was presented. Firstly

made the only one noisy image to be sparse by using the dictionary compression algorithm of K-SVD (Kernel Singular Value Decomposition). Secondly

obtained the first-time denoised image by using the redundant information. Finally

made both the first-time denoised image and original noisy image as the multiple observations for ICA separation. It could be seen that the sparse image obtained by proposed method was more exact than that by using only a dictionary compression algorithm of Nlmeans (Non-Local means). The result obtained shows that when the Gauss white noise's standard deviation σ is in the range of 20-45

the proposed method is better than either K-SVD algorithm or NLmeans algorithm

and the denoised image's PSNR (peak signal to noise ratio) is 1.4 times larger than that of the original noisy image.

关键词

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references

SOROUCHYARI E. Blind separation of sources, Part Ⅲ:Stability analysis[J]. Signal Processing, 1991, 24(1):21-29.

吴禄慎, 史皓良, 陈华伟.基于特征信息分类的三维点数据去噪[J].光学精密工程, 2016, 24(6):1465-1473.

WU L SH, SHI H L, CHEN H W. Denoising of three-dimensional point data based on classification of feature information[J]. Opt. Precision Eng., 2016, 24(6):1465-1473. (in Chinese)

OJA E, HYVÄRINEN A, HOYER P. Image feature extraction and denoising by sparse coding[J]. Pattern Analysis & Applications, 1999, 2(2):104-110.

VENTURA R M F I, VANDERGHEYNST P, FROSSARD P. Low-rate and flexible image coding with redundant representations[J]. IEEE Transactions on Image Processing, 2006, 15(3):726-739.

李新春, 于抒平, 王波.一种改进的非局部均值算法[J].计算机工程与应用, 2016, 52(5):185-189.

LI X CH, YU SH P, WANG B. Improved non-local means algorithm[J]. Computer Engineering and Applications, 2016, 52(5):185-189. (in Chinese)

BUADES A, COLL B, MOREL J M. Image denoising methods. A new nonlocal principle[J]. Siam Review, 2010, 52(1):113-147.

BUADES A, COLL B, MOREL J M. The staircasing effect in neighborhood filters and its solution[J]. IEEE Transactions on Image Processing, 2006, 15(6):1499-505.

DE LA Rosa J I, VILLA-HERNÁNDEZ J, CORTEZ J, et al .. On the comparison of different kernel functionals and neighborhood geometry for nonlocal means filtering[J]. Multimedia Tools and Applications, 2017, 77(1):1205-1235.

蔡斌, 刘卫, 郑重, 等.一种改进的非局部均值去噪算法[J].模式识别与人工智能, 2016, 29(1):1-10.

CAI B, LIU W, ZHENG ZH, et al .. An improved Non-Local means denoising algorithm[J]. Pattern Recognition and Artificial Intelligence, 2016, 29(1):1-10. (in Chinese)

THEPADE S D, GARG R H, GHEWADE S A, et al . . Performance assessment of assorted similarity measures in gray image colorization using LBG vector quantization algorithm[C]. International Conference on Industrial Instrumentation and Control , IEEE , 2015: 332-337.

MALIK R K, SOLANKI K. Fastica based blind source separation for CT imaging under noise conditions[J]. International Journal of Advances in Engineering & Technology, 2012, 5(1):47-55.

KULSOOM A, XIAO D, UR REHMAN Y A, et al .. An efficient and noise resistive selective image encryption scheme for gray images based on chaotic maps and DNA complementary rules[J]. Multimedia Tools and Applications, 2016, 75(1):1-23.

ROBERTS I, KAHN J M, BOERTJES D. Convex channel power optimization in nonlinear WDM systems using Gaussian noise model[J]. Journal of Lightwave Technology, 2016, 34(13):3212-3222.

JAKUBISIN D J, BUEHRER R M. Approximate joint MAP detection of Co-channel signals in non-Gaussian noise[J]. IEEE Transactions on Communications, 2016, 64(10):4224-4237.

UR REHMAN Y A, TARIQ M, SATO T. A novel energy efficient object detection and image transmission approach for wireless multimedia sensor networks[J]. IEEE Sensors Journal, 2016, 16(15):5942-5949.

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