Chip Image Super-Resolution Reconstruction Based on Deep Learning

被引：0

作者：

Fan M. ^{[1
]}

Chi Y. ^{[2
]}

Zhang M. ^{[1
]}

Li Y. ^{[1
]}

机构：

[1] State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an

[2] Science and Technology on Reliability Physics and Application of Electronic Component Laboratory, The Fifth Electronics Research Institute of Ministry of Industry and Information Technology, Guangzhou

来源：

Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2019年 / 32卷 / 04期

基金：

中国博士后科学基金;

关键词：

Chip Hardware Trojan; Convolutional Neural Network; Iterative Back Projection; Super-Resolution Reconstruction;

D O I：

10.16451/j.cnki.issn1003-6059.201904008

中图分类号：

学科分类号：

摘要：

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. 2019, Science Press. All right reserved.

引用

页码：353 / 360

页数：7

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