Abstract

Ghost imaging (GI) is an unconventional optical imaging method making use of the correlation measurement between a test beam and a reference beam. GI using deep learning (GIDL) has earned increasing attention, as it can reconstruct images of high quality more effectively than traditional GI methods. It has been demonstrated that GIDL can be trained completely with simulation data, which makes it even more practical. However, most GIDLs proposed so far appear to have limited performance for random noise distributed patterns. This is because traditional GIDLs are sensitive to the under-estimation error but robust to the over-estimation error. An asymmetric learning framework is proposed here to tackle the unbalanced sensitivity to estimation errors of GIDL. The experimental results show that it can achieve much better reconstructed images than GIDL with a symmetric loss function, and the structural similarity index of GI is quadrupled for randomly selected objects.

© 2020 Optical Society of America

Ghost imaging of blurred object based on deep-learning

Zijin Zhang, Chunfang Wang, Wenlin Gong, and Dawei Zhang
Appl. Opt. 60(13) 3732-3739 (2021)

Sub-Nyquist computational ghost imaging with deep learning

Heng Wu, Ruizhou Wang, Genping Zhao, Huapan Xiao, Daodang Wang, Jian Liang, Xiaobo Tian, Lianglun Cheng, and Xianmin Zhang
Opt. Express 28(3) 3846-3853 (2020)

Computational ghost imaging via adaptive deep dictionary learning

Xiang Zhai, Zhengdong Cheng, Zhenyu Liang, Yi Chen, Yangdi Hu, and Yuan Wei
Appl. Opt. 58(31) 8471-8478 (2019)

Color computational ghost imaging by deep learning based on simulation data training

Zhan Yu, Yang Liu, Jinxi Li, Xing Bai, Zhongzhuo Yang, Yang Ni, and Xin Zhou
Appl. Opt. 61(4) 1022-1029 (2022)

High-speed computational ghost imaging based on an auto-encoder network under low sampling rate

Wei Feng, Xingyu Sun, Xiuhua Li, Junhui Gao, Xiaodong Zhao, and Daxing Zhao
Appl. Opt. 60(16) 4591-4598 (2021)