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射线检测图像的自适应多尺度积阈值降噪算法
          
Radiographic Testing Image Denoising Algorithm Using Adaptive Multiscale Product Threshold

摘    要
针对射线检测图像的高噪声、低对比度、图像模糊等特点,提出了一种射线图像的自适应多尺度积阈值降噪算法,解决了常用射线检测图像降噪算法存在的降噪效果差、图像模糊、缺陷边缘和细节丢失等问题。该算法利用噪声估计、多尺度、积阈值、小波等方法对射线检测图像进行降噪处理,获得了高质量的降噪图像。以实际的工业焊缝射线检测图像为例,将所提算法与常用的小波降噪、中值滤波、维纳滤波、小波中值等算法进行降噪对比研究。试验结果表明,所提算法不仅具有优异的降噪性能,而且能够较好地保留缺陷图像边缘、细节等重要特征。
标    签 射线检测图像   自适应降噪   多尺度   积阈值   radiographic testing image   adaptive denoising   multiscale   product threshold  
 
Abstract
In view of the characteristics of radiographic image, including high noise, poor contrast, image blur, and so on, an adaptive radiographic image denoising algorithm using multiscale products threshold is proposed in this paper. It can overcome the conventional radiographic image denoising algorithms' problems of poor denoising effect, blurring image and losing defect edges and details. In this algorithm, the ideas of noise level estimation, multiscale, products threshold and wavelet transform are skillfully used, and then the denoised image with high quality is obtained. Taking the real radiographic images of industrial weld testing for example, the denoising comparison experiments are performed between the proposed algorithm and the conventional algorithms, including wavelet denoising, median filtering, Wiener filtering and wavelet-median filtering. Moreover, the experimental results demonstrate that the proposed algorithm not only has excellent denoising performance, but also preserves the defect edges and details well.

中图分类号 TN911.73 TG115.28   DOI 10.11973/wsjc201809006

 
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所属栏目 试验研究

基金项目 中北大学自然科学研究基金资助项目(XJJ2016007,XJJ2017006);山西省应用基础研究资助项目(201701D221167)

收稿日期 2018/4/9

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备注李建素(1986-),博士,讲师,主要从事无损检测、光电检测、数字全息等方面的科研和教学工作

引用该论文: LI Jiansu,DANG Changying,ZENG Zhiqiang,WANG Rijun. Radiographic Testing Image Denoising Algorithm Using Adaptive Multiscale Product Threshold[J]. Nondestructive Testing, 2018, 40(9): 22~27
李建素,党长营,曾志强,王日俊. 射线检测图像的自适应多尺度积阈值降噪算法[J]. 无损检测, 2018, 40(9): 22~27


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参考文献
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