摘要
针对各向同性扩散算法容易模糊图像特征信息以及张量扩散算法在图像同质区域内的噪声点处易产生伪条纹的问题,提出了一种能有效反映图像局部结构特征的局部方差作为特征参数,并基于该特征参数将上述两种算法相结合的新算法。该算法在对图像不同性质的区域进行降噪时,能根据图像的局部统计信息来调整上述两种扩散算法在该区域的扩散速度,以充分利用上述两种算法的优点并避免其缺点。实验结果表明,所提算法较几种经典的算法能更有效地去除噪声和保护、增强图像的边缘等特征信息。
Aiming at the problem that isotropic algorithm can easily blur the feature of image information and that tensor-based algorithm will produce pseudo striations at noisy points within homogeneous regions,this paper proposed a feature parameter that was local variance of image,this parameter could effectively reflect local structural features of image,based on this parameter,it proposed a novel algorithm that combined the above two algorithms. The proposed algorithm could adaptively adjust to the diffusion rate of the above two diffusion algorithms according to local statistics of image when de-noising on different regions of the image in order to preserve the advantages and cancel the drawbacks existed in the two algorithms mentioned above. The experimental results show that the proposed algorithm compared with others classical algorithm can remove the noise effectively and preserve,enhance the image edges better.
出处
《计算机应用研究》
CSCD
北大核心
2014年第12期3876-3879,共4页
Application Research of Computers
基金
国家自然科学基金资助项目(11265007)
国家教育部回国人员科研启动基金资助项目(2010-1561)
云南省人培基金资助项目(KKSY201203030)
关键词
图像降噪
各向同性扩散算法
张量扩散算法
局部方差
特征参数
image de-noising
isotropic diffusion algorithm
tensor-based diffusion algorithm
local variance
feature parameter
