快速非局部均值滤波图像去噪

摘要

针对非局部平均(NLM)去噪算法复杂度过高,滤波过程中对图像信号产生过度平滑的问题,提出一种基于高斯主成分分析的快速NLM去噪算法.该方法首先对所有像素点的邻域进行高斯滤波降低噪声干扰,提高主成分分析的准确度,降低分解结果的维度,进而提高NLM算法中的块匹配效率和准确性,为提高去噪效果奠定基础.虽然该方法在NLM前加入了高斯预滤波和主成分分析,但是由于有效的降维,整体算法复杂度较传统NLM仍有减少,且算法性能有所提高.实验表明与传统的NLM算法相比,本文所提出的新算法不仅降低了计算复杂度,而且可以产生更好的去噪效果.%This paper presents an efficient image denoising method by using Gaussian Principle Component Analysis (GPCA) in conjunction with Non-Local Means (NLM).By taking into account of the noise feature,this method pre-filters the image before PCA analysis to improve the PCA efficiency and decrease the dimensionality of projected the vector.By applying GPCA to project the image patch into a lower-dimensional (LD) space,the accuracy of the similarity weights calculation for NLM can be improved with less computational complexity.Experiments show that our method performs well in terms of image visual fidelity as well as PSNR with a degree of detail preservation.