摘要
针对图像加性高斯白噪声,提出一种优化的自适应参数滤波算法。该算法以非局部欧氏中值(nonlocal Euclidean medians,NLEM)滤波算法为基础,根据含噪图像梯度幅值在一定噪声范围内服从Rayleigh分布这一特性,求得以梯度幅值和噪声标准差为自变量的二元自适应滤波参数,并将它引入到邻域的权值计算中。其次,噪声的变化影响着p范数回归的选择,在一定范围内以噪声标准差为自变量对参数p进行多项式拟合,得到自适应p范数回归。在自适应滤波参数基础上,用自适应p范数回归进一步改进NLEM滤波算法的1-范数回归。所选图像的实验结果表明,本文算法在一定噪声范围内不但获得满意的去噪效果,而且有效地减少人机交互程度。
For additive white Gaussian noise of an image, this paper proposes an optimized adaptive parame- ter filter algorithm. Based on the non-local Euclidean medians （NLEM） algorithm, according to the property that the noise image gradient amplitude obeys Rayleigh distribution within a certain noise range, we obtain a bi- nary adaptive filter parameter by regarding gradient amplitude and noise standard deviation as independent varia- bles. The adaptive filter parameter is introduced in the weight calculation of neighbors. Furthermore, the chan- ges of the noise affect the selections of the gp norm regression. Make p used polynomial fit with noise standard deviation in a certain range, and get adaptive gp norm regression. On the basis of adaptive filter parameters, g2 norm regression used in NLEM can be improved by using adaptive gp norm regression. It is verified that the new algorithm not only obtains satisfactory results of denoising in a certain noise range, but also reduces the degree of human-computer interaction effectively.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2015年第2期449-454,共6页
Systems Engineering and Electronics
基金
高等学校博士学科点专项科研基金(20136102110037)资助课题
