High-Quality Computational Ghost Imaging Using an Optimum Distance Search Method
| Autor: | Heng Wu, Xianmin Zhang, Jinqiang Gan, Chun-Ling Luo |
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| Rok vydání: | 2016 |
| Předmět: |
lcsh:Applied optics. Photonics
Mean squared error Computer science image reconstruction techniques compressive sensing Iterative reconstruction Ghost imaging computational imaging 01 natural sciences 010309 optics Optics Quality (physics) 0103 physical sciences lcsh:QC350-467 Electrical and Electronic Engineering 010306 general physics business.industry Detector Process (computing) lcsh:TA1501-1820 Atomic and Molecular Physics and Optics Compressed sensing Signal-to-noise ratio (imaging) business Algorithm lcsh:Optics. Light |
| Zdroj: | IEEE Photonics Journal, Vol 8, Iss 6, Pp 1-9 (2016) |
| ISSN: | 1943-0655 |
| DOI: | 10.1109/jphot.2016.2633867 |
| Popis: | We propose an optimum distance search method for realizing high-quality computational ghost imaging (CGI). The proposed method, which utilizes the advantages of compressive sensing and the CGI technique, is composed of two search steps. The first step is a coarse search, and the second is a fine search. By using the two-step search, an optimum distance can be obtained. The signal-to-noise ratio (SNR) and the relative mean square error (RMSE) are used as criteria during the search process. Both simulation and experimental results demonstrate that the proposed method can enhance imaging quality, and compressive CGI is more sensitive to distance variations than traditional CGI. The SNR and RMSE are improved when the object is at the optimum distance. |
| Databáze: | OpenAIRE |
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