改进的LPG-PCA的图像去噪方法

计算机科学 ›› 2015, Vol. 42 ›› Issue (Z6): 195-198.

改进的LPG-PCA的图像去噪方法

李旭光,崔丽鸿,黄守勇

北京化工大学理学院北京100029,北京化工大学理学院北京100029,北京化工大学理学院北京100029

出版日期:2018-11-14 发布日期:2018-11-14
基金资助:
本文受国家自然科学基金(61272028)资助

Improvement of LPG-PCA Method for Image Denoising

LI Xu-guang, CUI Li-hong and HUANG Shou-yong

Online:2018-11-14 Published:2018-11-14

摘要/Abstract

摘要： 主成分分析(PCA)变换能够去除信号之间的相关性,并且在PCA域中,很容易把信号和噪声区分出来。在对目标像素块进行处理前,首先要在一定的搜索域中寻找与其结构相似的局部像素块作为训练样本,对图像进行复制,使用双参数收缩算法对复制图像进行处理,然后使用在复制图像中对应的像素块之间的欧氏距离,来代替目标像素块与局部像素块之间的相似性,减小了噪声所带来的影响,对后续的PCA变换起到了重要作用。仿真实验表明,改进的LPG-PCA方法相对于改进之前,使图像的质量有了一定提高。

Abstract: The Principal Component Analysis(PCA) can remove the correlation between signals,and it is easy to distinguish the signal and noise.Before the processing of target pixel block,first of all,we should find the training samples in a certain local pixel search domain which are similar to the target pixel block.In this paper,we get the copy of the image,and use the Bivariate shrinkage denoising method(Bishrink) to manage it.After that,we use Euclidean distance of the corresponding pixel block to replace the similarity between the target pixel block and the local pixel block,which will reduce the effect of the noise,and play an important role in the follow-up PCA transformation.Simulation results show that,improved LPG-PCA method has the very big enhancement in the quality of the image compared with the method before improvement.

Key words: Image denoising,Local pixel grouping,Principal component analysis,Bivariate shrinkage denoising method

李旭光,崔丽鸿,黄守勇. 改进的LPG-PCA的图像去噪方法[J]. 计算机科学, 2015, 42(Z6): 195-198. https://doi.org/

LI Xu-guang, CUI Li-hong and HUANG Shou-yong. Improvement of LPG-PCA Method for Image Denoising[J]. Computer Science, 2015, 42(Z6): 195-198. https://doi.org/

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