纹理保留的PCA非局部均值改进降噪方法  

PCA-Nonlocal Means Image Denoising Method for Texture Preserving

作　　者：明勇[1,2] 罗学刚[1,3] 王华军[1] 杨强[1] 何东[3] 

机构地区：[1]成都理工大学地球物理学院,四川成都610059 [2]成都广播电视大学,四川成都610051 [3]攀枝花学院数学与计算机学院,四川攀枝花617000

出　　处：《信阳师范学院学报（自然科学版）》2015年第4期604-607,共4页Journal of Xinyang Normal University(Natural Science Edition)

基　　金：国家自然科学基金项目(61202195/F020502);四川省教育厅科研基金项目(13ZB0212);四川省大学生创新创业项目基金(201311360030)

摘　　要：针对PCA-NLM去噪方法容易丢失图像纹理细节的问题,提出一种基于纹理特征描述的改进PCA非局部均值去噪方法.基于局部结构张量的边缘纹理描述将图像划分为平坦区、边缘区和纹理区,根据边缘纹理特征值自适应地选取PCA维数和搜索区域以改进去噪效果.实验结果表明,该方法对纹理细节丰富的区域能更好地保留了图像纹理细节,降噪效果优于PCA-NLM方法.In consideration that PCA-NLM may easily lead to the loss of image texture in denoising,an improved PCA nonlocal means image denoising method was proposed based on the description of texture features.An image was divided into flat,edge and texture regions based on the description of edge textures of local structure tensor.Besides,PCA dimensions and search areas were adaptively selected according to the characteristic values of the edge textures to improve the denoising effect.The experimental results showed that this method could better retain the details of the image textures in the areas with abundant texture details and was more effective for denoising than that of PCA-NLM.

关 键 词：图像去噪 非局部均值 纹理特征 PCA 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]