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

Chen Zhang; Bincheng Zhu; Sebastian Semper; Andreas Breitbarth; Maik Rosenberger; Gunther Notni

doi:10.1117/12.2518594

13 May 2019 A comparative investigation on the use of compressive sensing methods in computational ghost imaging

Chen Zhang, Bincheng Zhu, Sebastian Semper, Andreas Breitbarth, Maik Rosenberger, Gunther Notni

Proceedings Volume 10990, Computational Imaging IV; 109900R (2019) https://doi.org/10.1117/12.2518594
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

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.

Conference Presentation

Citation Download Citation

Chen Zhang, Bincheng Zhu, Sebastian Semper, Andreas Breitbarth, Maik Rosenberger, and Gunther Notni "A comparative investigation on the use of compressive sensing methods in computational ghost imaging", Proc. SPIE 10990, Computational Imaging IV, 109900R (13 May 2019); https://doi.org/10.1117/12.2518594

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