摘要
传统非局部均值滤波算法中使用指数型加权核函数,容易导致图像细节因过度平滑而变得模糊。为此,在指数型加权核函数的基础上,采用余弦系数加权的高斯核函数,设计一种改进的非局部均值图像去噪算法,并将其应用于加权系数计算中。实验结果表明,该算法的去噪性能优于传统算法,且能更好地保留原图像的细节信息,峰值信噪比最大可以提升1.6 dB。
Aiming at the problem of the over-smoothness and blurs the details, which are caused by exponential kernel function used in original non-local means algorithm, this paper proposes a cosine Ganssian kernel function based on exponential kernel function and combined with a cosine coefficient and Gaussian kernel. It is used in the weight-computing of the improved algorithm. Experimental results show the algorithm has a superior denoising performance than the original one, especially with detail information in the image, and PSNR can be improved by 1.6 dB at most.
出处
《计算机工程》
CAS
CSCD
2012年第4期199-201,207,共4页
Computer Engineering
基金
国家自然科学基金资助项目(51035008)
关键词
图像处理
图像去噪
非局部均值
加权平均
高斯噪声
加权核函数
image processing
image denoising
non-local means
weighted average
Gaussian noise
weighted kernel function
