Fourier-space Diffractive Deep Neural Network

被引：307

作者：

Yan, Tao ^{[1
]}

Wu, Jiamin ^{[1
]}

Zhou, Tiankuang ^{[1
,2
]}

Xie, Hao ^{[1
]}

Xu, Feng ^{[3
]}

Fan, Jingtao ^{[1
]}

Fang, Lu ^{[2
]}

Lin, Xing ^{[1
,4
]}

Dai, Qionghai ^{[1
]}

机构：

[1] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China

[2] Tsinghua Univ, Tsinghua Shenzhen Int Grad Sch, Shenzhen 518055, Peoples R China

[3] Tsinghua Univ, Sch Software, Beijing 100084, Peoples R China

[4] Univ Calif Los Angeles, Dept Elect & Comp Engn, Los Angeles, CA 90095 USA

来源：

PHYSICAL REVIEW LETTERS | 2019年 / 123卷 / 02期

基金：

中国国家自然科学基金;

关键词：

SALIENT OBJECT DETECTION;

D O I：

10.1103/PhysRevLett.123.023901

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

In this Letter we propose the Fourier-space diffractive deep neural network (F-(DNN)-N-2) for all-optical image processing that performs advanced computer vision tasks at the speed of light. The F-(DNN)-N-2 is achieved by placing the extremely compact diffractive modulation layers at the Fourier plane or both Fourier and imaging planes of an optical system, where the optical nonlinearity is introduced from ferroelectric thin films. We demonstrated that F-(DNN)-N-2 can be trained with deep learning algorithms for all-optical saliency detection and high-accuracy object classification.

引用

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