摘要
对非局部平均去噪算法提出了以下改进:首先,利用图像中具有对称结构的性质,在相似性邻域的比较中引入邻域的对称变换,更好地利用了图像的自相似性质;其次,提出一种基于图像灰度分布统计特性的滤波参数选取方法,能够根据不同像素的特点自适应地选取滤波参数;此外,利用非局部平均算法能有效地保护图像结构信息的性质,提出一种两级非局部平均去噪方法.对测试图像去噪的实验结果表明,与原始算法相比,提出的改进方法能够在保护图像结构信息的前提下更有效地去除噪声,峰值信噪比最多可以提高5.9dB,去噪效果优于BM-3D方法.
Three modifications are proposed for the Non-Local Means（NLM） de-noising algorithm.The symmetric property existing in many images is exploited first,symmetric transformations are introduced in neighborhood comparison so that image self-similarity property can be made better use of;Then an adaptive filtering parameter selection method is proposed based on image intensity statistics;Besides,a two-stage Non-Local Means filtering method is proposed adopting the structure preserving property of NLM algorithm.De-noising results for test images demonstrate that compared with the original algorithm,the modified one can remove noise more efficiently with image structures well preserved,the PSNR can be improved by 5.9dB at most,and the de-noising performance outperforms that of the BM-3D algorithm.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2010年第4期923-928,共6页
Acta Electronica Sinica
基金
国家自然科学基金(No.30870666)
国防科技预研基金(No.9140A17020508JW2401)
关键词
非局部平均
对称变换
自适应权值
去噪
non-local means
symmetric transform
adaptive weighting
de-noising
