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Proceedings Paper

Colored adaptive compressed imaging using color space conversion
Author(s): Yiyun Yan; Huidong Dai; Jin Gao; Chaowei Li; Xingjiong Liu; Weiji He; Qian Chen; Guohua Gu
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Paper Abstract

Computational ghost imaging (CGI) is mainly used to reconstruct grayscale images at present and there are few researches aiming at color images. In this paper, we both theoretically and experimentally demonstrate a colored adaptive compressed imaging method. Benefiting from imaging in YUV color space, the proposed method adequately exploits the sparsity of U, V components in the wavelet domain, the interdependence between luminance and chrominance, and the human visual characteristics. The simulation and experimental results show that our method greatly reduces the measurements required, and offers better image quality compared to recovering red (R), green (G) and blue (B) components separately in RGB color space. As the application of single photodiode increases, our method shows great potential in many fields.

Paper Details

Date Published: 10 February 2017
PDF: 5 pages
Proc. SPIE 10250, International Conference on Optical and Photonics Engineering (icOPEN 2016), 102502R (10 February 2017); doi: 10.1117/12.2266839
Show Author Affiliations
Yiyun Yan, Nanjing Univ. of Science and Technology (China)
Huidong Dai, Nanjing Univ. of Science and Technology (China)
Jin Gao, Northwest Institute of Mechanical and Electrical Engineering (China)
Chaowei Li, Northwest Institute of Mechanical and Electrical Engineering (China)
Xingjiong Liu, Nanjing Univ. of Science and Technology (China)
Weiji He, Nanjing Univ. of Science and Technology (China)
Qian Chen, Nanjing Univ. of Science and Technology (China)
Guohua Gu, Nanjing Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 10250:
International Conference on Optical and Photonics Engineering (icOPEN 2016)
Anand Krishna Asundi; Xiyan Huang; Yi Xie, Editor(s)

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