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Fourier-space Diffractive Deep Neural Network.
Yan, Tao; Wu, Jiamin; Zhou, Tiankuang; Xie, Hao; Xu, Feng; Fan, Jingtao; Fang, Lu; Lin, Xing; Dai, Qionghai.
Afiliação
  • Yan T; Department of Automation, Tsinghua University, Beijing, 100084, People's Republic of China.
  • Wu J; Department of Automation, Tsinghua University, Beijing, 100084, People's Republic of China.
  • Zhou T; Department of Automation, Tsinghua University, Beijing, 100084, People's Republic of China.
  • Xie H; Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China.
  • Xu F; Department of Automation, Tsinghua University, Beijing, 100084, People's Republic of China.
  • Fan J; School of Software, Tsinghua University, Beijing 100084, People's Republic of China.
  • Fang L; Department of Automation, Tsinghua University, Beijing, 100084, People's Republic of China.
  • Lin X; Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China.
  • Dai Q; Department of Automation, Tsinghua University, Beijing, 100084, People's Republic of China.
Phys Rev Lett ; 123(2): 023901, 2019 Jul 12.
Article em En | MEDLINE | ID: mdl-31386516
ABSTRACT
In this Letter we propose the Fourier-space diffractive deep neural network (F-D^{2}NN) for all-optical image processing that performs advanced computer vision tasks at the speed of light. The F-D^{2}NN 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-D^{2}NN can be trained with deep learning algorithms for all-optical saliency detection and high-accuracy object classification.

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2019 Tipo de documento: Article