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Proceedings Paper

A comparative investigation on the use of compressive sensing methods in computational ghost imaging
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Paper Abstract

Usually, a large number of patterns are needed in the computational ghost imaging (CGI). In this work, the possibilities to reduce the pattern number by integrating compressive sensing (CS) algorithms into the CGI process are systematically investigated. Based on the different combinations of sampling patterns and image priors for the L1-norm regularization, different CS-based CGI approaches are proposed and implemented with the iterative shrinkage thresholding algorithm. These CS-CGI approaches are evaluated with various test scenes. According to the quality of the reconstructed images and the robustness to measurement noise, a comparison between these approaches is drawn for different sampling ratios, noise levels, and image sizes.

Paper Details

Date Published: 13 May 2019
PDF: 10 pages
Proc. SPIE 10990, Computational Imaging IV, 109900R (13 May 2019); doi: 10.1117/12.2518594
Show Author Affiliations
Chen Zhang, Technische Univ. Ilmenau (Germany)
Bincheng Zhu, Technische Univ. Ilmenau (Germany)
Sebastian Semper, Technische Univ. Ilmenau (Germany)
Andreas Breitbarth, Technische Univ. Ilmenau (Germany)
Maik Rosenberger, Technische Univ. Ilmenau (Germany)
Gunther Notni, Technische Univ. Ilmenau (Germany)
Fraunhofer-Institut für Angewandte Optik und Feinmechanik (Germany)

Published in SPIE Proceedings Vol. 10990:
Computational Imaging IV
Abhijit Mahalanobis; Lei Tian; Jonathan C. Petruccelli, Editor(s)

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