中文摘要：

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.

英文摘要：

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.