基于图像分割的非局部均值去噪算法

摘要

针对传统非局部均值(NLM)算法的滤波参数非自适应及去噪后边缘易模糊的缺点,提出一种基于图像分割的非局部均值去噪算法.该算法分为两个阶段:第一阶段根据噪声大小及图像纹理自适应确定滤波参数的值,并采用传统非局部均值算法得到去噪结果图;第二阶段根据像素点方差的不同,将该去噪结果图分为细节区域和背景区域,再对属于不同区域的图像块分别去噪,同时为了更有效地去除噪声,还采用了反向投影的方式,充分利用了第一阶段方法噪声中残留的结构信息.实验结果表明,与传统非局部均值算法及其三种改进算法相比,所提算法的峰值信噪比(PSNR)及结构相似性(SSIM)更高,纹理细节和边缘结构更完整,图像更清晰,本真信息保留更完整.%Focusing on the problems of non-adaption of filtering parameters and edge blur of Non-Local Means (NLM) algorithm,an improved NLM denoising algorithm based on image segmentation was proposed.The proposed algorithm is composed of two phases.In the first phase,the filtering parameter was determined according to the noise level and image structure,and traditional NLM algorithm was used to remove the noise and generate the rough clean image.In the second phase,the estimated clean image was divided into detailed region and background region based on pixel variance,and the image patches belonged to different regions were denoised separately.To effectively remove the noise,the back projection was utilized to make full use of the residual structure from the method noise of the first phase.The experimental results show that compared with traditional NLM and three NLM-improved algorithms,the proposed algorithm achieves higher Peak Signal-to-Noise Ratio (PSNR) and Structural SIMilarity (SSIM),while maintaining more structure details and edges,making the denoised image clear and retaining the complete real information.