10 February 2017 Colored adaptive compressed imaging using color space conversion
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Proceedings Volume 10250, International Conference on Optical and Photonics Engineering (icOPEN 2016); 102502R (2017) https://doi.org/10.1117/12.2266839
Event: Fourth International Conference on Optical and Photonics Engineering, 2016, Chengdu, China
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.
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Yiyun Yan, Huidong Dai, Jin Gao, Chaowei Li, Xingjiong Liu, Weiji He, Qian Chen, Guohua Gu, "Colored adaptive compressed imaging using color space conversion", Proc. SPIE 10250, International Conference on Optical and Photonics Engineering (icOPEN 2016), 102502R (10 February 2017); doi: 10.1117/12.2266839; https://doi.org/10.1117/12.2266839
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