GAUSSIAN PRINCIPLE COMPONENTS FOR NONLOCAL MEANS IMAGE DENOISING

作     者：Li Xiangping Wang Xiaotian Shi Guangming(Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education of China,Xidian University,Xi’an 710071,China) 

出 版 物：《Journal of Electronics(China)》 (电子科学学刊(英文版))

年 卷 期：2011年第Z1期

页      面：539-547页

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：Supported by the National Natural Science Foundation of China (No. 60776795,60736043,60902031,and 60805012) the Research Fund for the Doctoral Program of Higher Education of China (No. 200807010004,20070701023) the Fundamental Research Funds for the Central Universities of China (No. JY10000902028) 

主　　题：Image denoising NonLocal Means(NLM) Gaussian filter Principle Component Analysis(PCA) 

摘      要：NonLocal Means(NLM),taking fully advantage of image redundancy,has been proved to be very effective in noise ***,high computational load limits its wide *** on Principle Component Analysis(PCA),Principle Neighborhood Dictionary(PND) was proposed to reduce the computational load of ***,as the principle components in PND method are computed directly from noisy image neighborhoods,they are prone to be inaccurate due to the presence of *** this paper,an improved scheme for image denoising is *** scheme is based on PND and uses preprocessing via Gaussian filter to eliminate the influence of *** is then used to project those filtered image neighborhood vectors onto a lower-dimensional *** the preproc-essing process,the principle components computed are more accurate resulting in an improved de-noising performance.A comparison with some NLM based and state-of-art denoising methods shows that the proposed method performs well in terms of Peak Signal to Noise Ratio(PSNR) as well as image visual *** experimental results demonstrate that our method outperforms existing methods both subjectively and objectively.