Multi-regularization image restoration method for X-ray images of integrated circuit

MA Ge; LIN Sen; LI Zhifu; ZHAO Zhijia; ZOU Tao

doi:10.37188/OPE.20223022.2913

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Multi-regularization image restoration method for X-ray images of integrated circuit

Information Sciences | 更新时间：2022-11-24

- Multi-regularization image restoration method for X-ray images of integrated circuit
- Optics and Precision Engineering Vol. 30, Issue 22, Pages: 2913-2922(2022)
- 作者机构：
  
  广州大学机械与电气工程学院，广东广州 510006
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20223022.2913
  CLC： TP394.1;TH691.9
- Received：29 April 2022，
  
  Revised：23 May 2022，
  
  Published：25 November 2022
- 稿件说明：
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马鸽,林森,李致富等.集成电路X射线图像的多正则化图像复原[J].光学精密工程,2022,30(22):2913-2922.

MA Ge,LIN Sen,LI Zhifu,et al.Multi-regularization image restoration method for X-ray images of integrated circuit[J].Optics and Precision Engineering,2022,30(22):2913-2922.
马鸽,林森,李致富等.集成电路X射线图像的多正则化图像复原[J].光学精密工程,2022,30(22):2913-2922. DOI： 10.37188/OPE.20223022.2913.

MA Ge,LIN Sen,LI Zhifu,et al.Multi-regularization image restoration method for X-ray images of integrated circuit[J].Optics and Precision Engineering,2022,30(22):2913-2922. DOI： 10.37188/OPE.20223022.2913.

摘要

针对集成电路X射线图像噪声强烈、对比度低的特点，本文充分考虑图像边缘细节和平滑区域在细节保持和噪声去除上的不同需求，提出一种多正则化图像复原方法。该方法基于快速傅里叶变换下的高斯高通滤波和高斯低通滤波获取集成电路X射线图像的边缘细节结果和平滑滤波结果作为正则化图像复原的观测图像，充分利用

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正则项在细节保持以及全变分（Total Variation，

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）正则项在噪声去除上的优越性设计了一种

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混合正则化模型，解决整幅图像采用单一正则化项造成的细节过度平滑缺失或去噪效果差的问题。标准自然光图像和集成电路的X射线图像两个系列的实验表明了本文方法能够在有效去除噪声的同时保留更多的细节信息，为后续集成电路的缺陷检测奠定基础。

Abstract

The X-ray images of Integrated Circuits （IC） generally have high noise and low contrast characteristics. Considering the different needs in detail preservation and noise removal of edge details and smooth regions， this paper proposes a multi-regularization image restoration method. Firstly， by employing a Fourier transform based Gauss high-pass filter and Gauss low-pass filter， the edge detail and smoothing filter results were obtained as new observed images for image restoration. Then， a TV-

mixed regularization model， which takes full use of the advantages of

regularization term in detail preservation and Total Variation （TV） regularization term in noise removal， was designed. The model is capable of addressing the problem of excessive smoothing or defective denoising caused by a single regularization term. Experiments on standard and IC X-ray images show that the proposed method can effectively remove noise while retaining more details， laying a foundation for subsequent defect detection of IC.

关键词

Keywords

references

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