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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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

Received: 30 July 2018

ZTFLH:

TP 391.41

Fund:

Supported by Natural Science Plan Basic Research in Shaanxi Province of China(No.2018JQ6028), Fundamental Research Funds for the Central Universities(No.XJS17109,JBX180102), Chinese Postdoctoral Science Foundation(No.2017M623125), Open Fund for Key Laboratory of Reliability Physics and Its Application of Technology Electronical Component(No.17D03-ZHD201701)

About author:: FAN Mingming, master student. Her research interests include image processing and pattern recognition.CHI Yuan, Ph.D., engineer. His research interests include integrated circuits design, chip reliability and chip security.ZHANG Mingjin(Corresponding author), Ph.D., lecturer. Her research interests include pattern recognition and artificial intelligence.LI Yunsong, Ph.D., professor. His research interests include image processing, chip design and high performance computing.

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	LI Yunsong

Cite this article:

FAN Mingming,CHI Yuan,ZHANG Mingjin等. Chip Image Super-Resolution Reconstruction Based on Deep Learning[J]. , 2019, 32(4): 353-360.

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

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