微焦点X射线图像乘性加性混合噪声的去除

高红霞; 吴丽璇; 徐寒; 康慧; 胡跃明

doi:10.3788/OPE.20142211.3100

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微焦点X射线图像乘性加性混合噪声的去除

信息科学 | 更新时间：2020-08-13

- 微焦点X射线图像乘性加性混合噪声的去除
- Denoising method of micro-focus X-ray images corrupted with mixed multiplicative and additive noises
- 光学精密工程 2014年22卷第11期页码：3100-3113
- 作者机构：
  
  1. 华南理工大学自动化科学与工程学院,广东广州,510640
  2. 华南理工大学精密电子制造装备教育部工程研究中心,广东广州,510640
  3. 广东技术师范学院自动化学院,广东广州,510665
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(No.61403146)();国家863高技术研究发展计划资助项目(No.2012AA041312)();国家科技重大专项02专项子课题&qu
- DOI：10.3788/OPE.20142211.3100
  中图分类号： TP242.6;TP391.4
- 收稿日期：2014-05-23，
  
  修回日期：2014-06-23，
  
  纸质出版日期：2014-11-25
- 稿件说明：
移动端阅览
高红霞, 吴丽璇, 徐寒等. 微焦点X射线图像乘性加性混合噪声的去除[J]. 光学精密工程, 2014,22(11): 3100-3113

GAO Hong-xia, WU Li-xuan, XU Han etc. Denoising method of micro-focus X-ray images corrupted with mixed multiplicative and additive noises[J]. Editorial Office of Optics and Precision Engineering, 2014,22(11): 3100-3113
高红霞, 吴丽璇, 徐寒等. 微焦点X射线图像乘性加性混合噪声的去除[J]. 光学精密工程, 2014,22(11): 3100-3113 DOI： 10.3788/OPE.20142211.3100.

GAO Hong-xia, WU Li-xuan, XU Han etc. Denoising method of micro-focus X-ray images corrupted with mixed multiplicative and additive noises[J]. Editorial Office of Optics and Precision Engineering, 2014,22(11): 3100-3113 DOI： 10.3788/OPE.20142211.3100.

摘要

考虑微焦点X射线仪成像信噪比低

混合噪声污染严重等问题

提出了一种乘性、加性混合噪声去除方法。首先

建立了含乘性、加性混合噪声的图像模型;其次

基于总变分和稀疏表示原理分别构造了滤除加性噪声和乘性噪声的目标函数;最后

应用显式差分算法和梯度投影算法分步滤除加性噪声和乘性噪声。实验结果显示

与总变分去加性噪声方法相比

该方法处理后的图像平滑区域均值与标准差比(MSR)平均提升了10.9%

细节区域拉普拉斯梯度模(LS)平均提升了15.6%。这些结果表明:本文算法不仅有效滤除了微焦点X射线图像的混合噪声

并且较好地保留了图像细节特征

能够满足集成电路内部缺陷检测对图像平滑度和细节清晰度的要求。

Abstract

In consideration of the lower imaging Signal to Noise Ratio(SNR) and serious mixed noise of a micro-focus X-ray inspector

a denoising method was proposed for the images corrupted with mixed multiplicative noise and additive noise. Firstly

an image model was established to represent the micro-focus X-ray images with mixed multiplicative and additive noises. Then

to remove the mixed noises

the objective functions were proposed based on the principle of total variation and sparse representation. Finally

the multiplicative noise and the additive noise were removed by explicit difference method and gradient projection in steps. Experiment results show that the proposed method enhances the Mean to Standard deviation Ratio(MSR) of the images by 10.9% in smooth areas

the Laplacian Sum(LS) by 15.6% in detail areas as compared with total variation algorithm for the additive noise model. The experiments demonstrate that the proposed method not only removes the mixed noises in X-ray image but also retains the details of the image edge. It meets the requirements of integrated circuit detection for image smoothness and detail definition.

关键词

Keywords

references

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