Deep-learning-based ghost imaging

被引：412

作者：

Lyu, Meng ^{[1
,2
]}

Wang, Wei ^{[1
,2
]}

Wang, Hao ^{[1
,2
]}

Wang, Haichao ^{[1
,2
]}

Li, Guowei ^{[1
,2
]}

Chen, Ni ^{[1
,2
]}

Situ, Guohai ^{[1
,2
]}

机构：

[1] Chinese Acad Sci, Shanghai Inst Opt & Fine Mech, Shanghai 201800, Peoples R China

[2] Univ Chinese Acad Sci, Beijing 100049, Peoples R China

来源：

SCIENTIFIC REPORTS | 2017年 / 7卷

基金：

上海市自然科学基金; 中国国家自然科学基金; 中国博士后科学基金;

关键词：

D O I：

10.1038/s41598-017-18171-7

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

In this manuscript, we propose a novel framework of computational ghost imaging, i.e., ghost imaging using deep learning (GIDL). With a set of images reconstructed using traditional Gl and the corresponding ground-truth counterparts, a deep neural network was trained so that it can learn the sensing model and increase the quality image reconstruction. Moreover, detailed comparisons between the image reconstructed using deep learning and compressive sensing shows that the proposed GIDL has a much better performance in extremely low sampling rate. Numerical simulations and optical experiments were carried out for the demonstration of the proposed GIDL.

引用

页数：6

共 33 条

[1]

[Anonymous], ARXIV171106810

[2] Experiment in lensless ghost imaging with thermal light [J].