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1. 长春理工大学, 电信学院,吉林 长春,130022
2. 长春理工大学, 理学学院,吉林 长春,130022
收稿日期:2002-07-24,
修回日期:2002-11-08,
纸质出版日期:2002-11-20
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但唐仁, 田景全, 高延军, 李野, 姜德龙, 端木庆铎, 富丽晨. 低强度X射线影像系统的噪声分析及图像去噪处理[J]. 发光学报, 2002,23(6): 615-618
DAN Tang-ren, TIAN Jing-quan, GAO Yan-jun, LI Ye, JIANG De-long, DUANMU Qing-duo, FU Li-chen. Imaging Filter and Noise Analyse Based on Low Intense X-ray Image System[J]. Chinese Journal of Luminescence, 2002,23(6): 615-618
但唐仁, 田景全, 高延军, 李野, 姜德龙, 端木庆铎, 富丽晨. 低强度X射线影像系统的噪声分析及图像去噪处理[J]. 发光学报, 2002,23(6): 615-618 DOI:
DAN Tang-ren, TIAN Jing-quan, GAO Yan-jun, LI Ye, JIANG De-long, DUANMU Qing-duo, FU Li-chen. Imaging Filter and Noise Analyse Based on Low Intense X-ray Image System[J]. Chinese Journal of Luminescence, 2002,23(6): 615-618 DOI:
新型低强度X射线影像系统主要是由平板式单近贴静电聚焦X射线像增强器和CCD数据采集系统构成。根据系统的组成
分析了图像噪声来源
指出了它们的噪声整体为泊松分布规律的随机白噪声
局部也有正负脉冲干扰等特点
以此提出了处理该图像噪声的"多帧平均滤波+极值中值滤波"的复合算法。即先根据随机噪声互不相关的特点
将多幅图像叠加平均
突出有用信息
压缩噪声。再在改进标准中值滤波基础上
采用极值中值滤波
更好地去除噪声
保留细节。通过对峰值信噪比的计算表明
该方法明显优于任何单一算法
取得较好效果。
New structure of a low intensity X-ray image system is mainly made of plane plate mode X-ray intensifier of single proximity focus and CCD data acquisition and processing system. By the system composition
the paper analyses the image noise source of low X-ray imaging system
and points out that the random noise is white noise which is obeyed by Poisson distribution in the whole body
yet the positive-negative interfering impulse is excited in the some locality. Then the compound methods of the "multi-frame mean + extremum median filter" is submitted which deals with the imaging noise. Firstly
some frame images is superimposed
then mean image is calculated from those images
which is under the principle of noise non-correlation. By the method
the information is enhanced and the noise is compressed. Secondly
based on the standard median filtering algorithm
the extremum median filter is ordered as much as possible to preserving the detail of the image when the noise are removed. That is to say
all the pixels are separated into signal and noise pixels according to the decision criterion given in the following; then
noise pixels are replaced with the median value of their neighborhood in the input image. The decision criterion: if a pixels value is the extremum (max or min) of its neighborhood
it is a noise pixel; else it is a signal pixel. This decision criterion is under such an assumption: inherent relationships exist among neighbor pixels. If a pixels value is far higher or lower than the others' value of its neighborhood
we may consider that it had been contaminated with noise. Else
if it is similar to the others
we consider that it represents an effective signal. By the calculation of PSNP
the methods are supper to any single method greatly. And the effect of image filter is satisfied.
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