非局部均值噪声预测的独立成分分析降噪研究

孙京阳; 喻春雨; 董仕佳

doi:10.3788/OPE.20182602.0511

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非局部均值噪声预测的独立成分分析降噪研究

信息科学 | 更新时间：2020-07-05

- 非局部均值噪声预测的独立成分分析降噪研究
- Noise reduction of independent component analysis based on NLmeans noise prediction
- 光学精密工程 2018年26卷第2期页码：511-516
- 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：
  
  [ "孙京阳(1991-), 男, 山东淄博人, 主要从事X射线成像及图像降噪算法研究。E-mail:15062200529m@sina.cn" ]
  [ "喻春雨(1976-)，女，辽宁沈阳人，博士，副教授，2006年于南京理工大学获光学工程博士学位，2006~2008年在北京大学信息科学技术学院从事博士后工作，2014~2015年在宾夕法尼亚大学医学院访学，主要从事X射线成像新方法研究。E-mail: yucy@njupt.edu.cn" ]
- 基金信息：
  
  江苏省自然科学基金(BK20140876);南京邮电大学科研基金资助项目(NY214057);南京邮电大学科研基金资助项目(NY215086)
- DOI：10.3788/OPE.20182602.0511
  中图分类号： TP391.41
- 收稿日期：2017-06-26，
  
  录用日期：2017-9-5，
  
  纸质出版日期：2018-02-25
- 稿件说明：
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孙京阳, 喻春雨, 董仕佳. 非局部均值噪声预测的独立成分分析降噪研究[J]. 光学精密工程, 2018,26(2):511-516.

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.
孙京阳, 喻春雨, 董仕佳. 非局部均值噪声预测的独立成分分析降噪研究[J]. 光学精密工程, 2018,26(2):511-516. DOI： 10.3788/OPE.20182602.0511.

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.

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