摘要
井下视频图像总体上色调偏暗,目标信息对比度不高,且由于井下存在大量粉尘,导致图像中存在高密度的随机噪声,增加了图像判读与分析的困难。为此,首先对井下视频图像进行多尺度脊波分解,对分解得到的低频系数和高频系数分别进行系数重构,得到井下视频图像的背景图像和细节图像;其次,根据背景图像灰度直方图的分布特征,采用高斯分布函数进行规定化处理;然后,在分析中值滤波(Median filtering,MF)特征的基础上,采用边界判别噪声检测方法(Boundary discrimination noise detection,BDND)对其进行改进,提出了一种基于BDND的改进中值滤波算法并用于处理细节图像,该算法根据细节图像局部区域灰度值的分布特征,设定2个自适应阈值t_1和t_2,将经过2次噪声检测后处于[t_1,t_2]区间内的灰度值对应的像素点标记为非噪声点,对其余像素点分别进行中值滤波;最后,对于直方图规定化处理后的背景图像和滤波后的细节图像进行叠加。采用C++语言对所提算法进行编程试验,结果表明,该算法对于不同模糊程度的井下视频图像均有较好的滤波效果,其性能相对于中值滤波、加权中值滤波、开关中值滤波等同类算法而言有了一定程度的提升。
The overall total of undermine video image is dark, the contrast of the information distributed in undermine video image is low, due to the large amount of dust existed in undermine, the undermine video image is polluted by the random noise with high density, so, the difficulties of image interpretation and analysis are increased. Firstly, the undermine video image is conducted multi-scale ridgelet decomposition, the low-frequency and high frequency coefficients are conducted inverse ridge- let reconstruction respectively, the background image and details image of the original undermine video image are obtained ; sec- ondly, according to the grey histogram distribution characteristics of the background image, the histogram specification method based on Gaussian distribution function is used to improve the visual effect of the background image;then, based on analyzing the characteristics of median filtering（MF） ,the boundary discrimination noise detection method（BDND） is adopted to improve the median filtering（MF） , a improved median filtering algorithm based on BDND is proposed, and it is used to deal with the details image,the two adaptive threshold values of t1 and t2 are set by the algorithm proposed in this paper based on the grey values distribution features of the local region of the details image, the grey values distributed in the range of[ t1, t2 ] corre- sponding pixel points are regarded as pixel points that are not polluted by random noises,the other pixel points are processed by median filtering algorithm respectively;finally, the background image processed by histogram specification method and the details image processed by the improved median filtering algorithm are superimposed. The C＋＋ language is used to write the computer programme of the improved median filtering algorithm, the experimental results show that the filtering effects of the undermine video image with different fuzzy degree of the algorithm proposed in this paper is superior to median filtering algo- rithm, weighted
出处
《金属矿山》
CAS
北大核心
2016年第1期142-145,共4页
Metal Mine
关键词
井下视频图像
脊波变换
边界判别噪声检测方法
中值滤波
高斯分布函数
Undermine video image, Ridgelet transform, Boundary discrimination noise detection method, Median filte-ring, Gaussian distribution function
