Open AccessSingular value decomposition ghost imaging
Author(s) -
Xue Zhang,
Xiangfeng Meng,
Xiulun Yang,
Yurong Wang,
Yongkai Yin,
Xianye Li,
Xiang Peng,
Wenqi He,
Guoyan Dong,
Hongyi Chen
Publication year - 2018
Publication title -
optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.26.012948
Subject(s) - ghost imaging , singular value decomposition , singular value , matrix decomposition , matrix (chemical analysis) , algorithm , iterative reconstruction , optics , inverse , mathematics , computer science , physics , artificial intelligence , geometry , eigenvalues and eigenvectors , materials science , quantum mechanics , composite material
The singular value decomposition ghost imaging (SVDGI) is proposed to enhance the fidelity of computational ghost imaging (GI) by constructing a measurement matrix using singular value decomposition (SVD) transform. After SVD transform on a random matrix, the non-zero elements of singular value matrix are all made equal to 1.0, then the measurement matrix is acquired by inverse SVD transform. Eventually, the original objects can be reconstructed by multiplying the transposition of the matrix by a series of collected intensity. SVDGI enables the reconstruction of an N-pixel image using much less than N measurements, and perfectly reconstructs original object with N measurements. Both the simulated and the optical experimental results show that SVDGI always costs less time to accomplish better works. Firstly, it is at least ten times faster than GI and differential ghost imaging (DGI), and several orders of magnitude faster than pseudo-inverse ghost imaging (PGI). Secondly, in comparison with GI, the clarity of SVDGI can get sharply improved, and it is more robust than the other three methods so that it yields a clearer image in the noisy environment.