基于深度学习的芯片图像超分辨率重建

doi:10.16451/j.cnki.issn1003-6059.201904008

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摘要

考虑到卷积神经网络可以通过训练过程引入图像的先验知识,文中提出基于深度学习的芯片图像超分辨率重建.利用卷积神经网络改善迭代反投影法的初始估计图像,通过迭代过程引入图像序列间的互补信息,建立芯片图像的样本集.实验表明,在不同放大倍数下,改进算法的客观评价指标平均值均较高,在芯片图像中的电路密集处,改进算法的主观视觉感受也较好.同时,文中算法适用于自然图像.

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	范明明
	池源
	张铭津
	李云松

关键词 ：超分辨率重建, 卷积神经网络, 迭代反投影, 芯片硬件木马

Abstract：

Since the convolutional neural networks can introduce the prior knowledge of the chip image in the training stage, a chip image super-resolution algorithm is proposed. A convolutional neural network is utilized to improve the initial reconstruction image of the iterative method, the complementary information between image sequences is employed through an iterative process and a chip sample set is built. Experimental results show that the proposed method produces clearer chip images with close packing and yields higher average values of the objective evaluation indicators. Furthermore, the proposed algorithm performs well on nature images.

Key words： Super-Resolution Reconstruction Convolutional Neural Network Iterative Back Projection Chip Hardware Trojan

收稿日期: 2018-07-30

ZTFLH:

TP 391.41

基金资助:

陕西省自然科学基础研究计划一般项目(No.2018JQ6028)、中央高校基本科研业务费项目(No.XJS17109,JBX180102)、中国博士后科学基金面上资助项目(No.2017M623125)、电子元器件可靠性物理及其应用技术重点实验室开放基金项目(No.17D03-ZHD201701)资助

作者简介: 范明明,硕士研究生,主要研究方向为图像处理、模式识别.E-mail:mmfanxidian@163.com.池源,博士,工程师,主要研究方向为集成电路设计、芯片可靠性、芯片安全性.E-mail:chiyuan4213@126.com.张铭津(通讯作者),博士,讲师,主要研究方向为模式识别、人工智能.E-mail:mjinzhang@xidian.edu.cn.李云松,博士,教授,主要研究方向为图像处理、芯片设计、高性能计算.E-mai:ysli@mail.xidian.edu.cn.

引用本文:

范明明, 池源, 张铭津, 李云松. 基于深度学习的芯片图像超分辨率重建[J]. 模式识别与人工智能, 2019, 32(4): 353-360. FAN Mingming, CHI Yuan, ZHANG Mingjin, LI Yunsong. Chip Image Super-Resolution Reconstruction Based on Deep Learning. , 2019, 32(4): 353-360.

链接本文:

http://manu46.magtech.com.cn/Jweb_prai/CN/10.16451/j.cnki.issn1003-6059.201904008 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2019/V32/I4/353

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