DCT子空间的非局部均值去噪算法

胡金蓉; 蒲亦非; 张意; 周激流

DCT子空间的非局部均值去噪算法

Nonlocal Means Denoising Algorithm Based on DCT Subspace

摘要

摘要: 在整个图像块像素灰度值向量空间中，非局部均值（nonlocal means，NLM）算法度量像素间的相似性不仅计算复杂度高，而且当噪声存在时还不能准确地计算出像素间的相似性权重值，影响了对图像冗余性质的利用，使得去噪结果图像对比度和清晰度低.针对NLM算法的这一缺陷，利用离散余弦变换（discrete cosine transform，DCT）的低数据相关性和高能量紧致性，将DCT与NLM算法相结合，对图像块进行DCT，并在DCT低频系数子空间内度量像素间的相似性.实验结果表明，与NLM算法相比，该方法能够在保护图像结构信息、对比度和清晰度的前提下更有效地去除噪声，峰值信噪比值一般可以提高1dB以上，运行时间不到NLM算法的1/10.

Abstract: Nonlocal means(NLM) has been becoming one of the most useful tools for image denoising.However,the computational cost is high due to the fact that calculation for similarity weights is performed in a full space of neighborhood patches.In addition,the calculation for its similarity weights has limited accuracy against noise when the noise standard deviation is large.In order to handle above-mentioned problems,we introduce a novel image denoising algorithm that integrates discrete cosine transform(DCT) into the NLM method,motivating from promising characteristics of DCT,such as low data correlation and high energy compaction,etc.Our novel NLM-DCTS algorithm is capable of improving quality and reducing the computational cost.Compared with NLM algorithm,the PSNR value can be improved more than 1 dB by NLM-DCTS,and the running time can be reduced to 1/10 of NLM's.

HTML全文

参考文献(0)

施引文献

资源附件(0)