Open AccessAdaptive compressive ghost imaging based on wavelet trees and sparse representation
Author(s) -
Wen-Kai Yu,
Mingfei Li,
XuRi Yao,
Xuefeng Liu,
LingAn Wu,
Guangtao Zhai
Publication year - 2014
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.22.007133
Subject(s) - ghost imaging , compressed sensing , computer science , computation , sparse approximation , wavelet , iterative reconstruction , optics , representation (politics) , artificial intelligence , computer vision , algorithm , physics , politics , political science , law
Compressed sensing is a theory which can reconstruct an image almost perfectly with only a few measurements by finding its sparsest representation. However, the computation time consumed for large images may be a few hours or more. In this work, we both theoretically and experimentally demonstrate a method that combines the advantages of both adaptive computational ghost imaging and compressed sensing, which we call adaptive compressive ghost imaging, whereby both the reconstruction time and measurements required for any image size can be significantly reduced. The technique can be used to improve the performance of all computational ghost imaging protocols, especially when measuring ultra-weak or noisy signals, and can be extended to imaging applications at any wavelength.