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

: Zhang, C.; Zhu, B.; Semper, S.; Breitbarth, A.; Rosenberger, M.; Notni, G.


Mahalanobis, A. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Computational Imaging IV : 14-15 April 2019, Baltimore, Maryland, United States
Bellingham, WA: SPIE, 2019 (Proceedings of SPIE 10990)
ISBN: 978-1-5106-2645-4
ISBN: 978-1-5106-2646-1
Paper 109900R, 10 S.
Conference "Computational Imaging" <4, 2019, Baltimore/Md.>
Fraunhofer IOF ()

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.