Open AccessSub-Nyquist computational ghost imaging with deep learning
Author(s) -
Heng Wu,
Ruizhou Wang,
Genping Zhao,
Huifang Xiao,
Daodang Wang,
Jian Liang,
Xiaobo Tian,
Lianglun Cheng,
Xianmin Zhang
Publication year - 2020
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.386976
Subject(s) - ghost imaging , nyquist–shannon sampling theorem , computer science , compressed sensing , image quality , nyquist rate , artificial intelligence , nyquist frequency , sampling (signal processing) , deep learning , iterative reconstruction , computer vision , optics , algorithm , image (mathematics) , physics , filter (signal processing)
We propose a deep learning computational ghost imaging (CGI) scheme to achieve sub-Nyquist and high-quality image reconstruction. Unlike the second-order-correlation CGI and compressive-sensing CGI, which use lots of illumination patterns and a one-dimensional (1-D) light intensity sequence (LIS) for image reconstruction, a deep neural network (DAttNet) is proposed to restore the target image only using the 1-D LIS. The DAttNet is trained with simulation data and retrieves the target image from experimental data. The experimental results indicate that the proposed scheme can provide high-quality images with a sub-Nyquist sampling ratio and performs better than the conventional and compressive-sensing CGI methods in sub-Nyquist sampling ratio conditions (e.g., 5.45%). The proposed scheme has potential practical applications in underwater, real-time and dynamic CGI.