Color ghost imaging via sparsity constraint and non-local self-similarity

Pengwei Wang; Chenglong Wang; Cuiping Yu; Shuai Yue; Wenlin Gong; Shensheng Han

doi:doi:10.3788/COL202119.021102

Chinese Optics Letters, 2021, 19 (2): 021102, Published Online: Jan. 4, 2021

Color ghost imaging via sparsity constraint and non-local self-similarity Download： 809次

Pengwei Wang ^1,2Chenglong Wang ¹Cuiping Yu ³Shuai Yue ³Wenlin Gong ^1,2,*Shensheng Han ^1,2,4

Author Affiliations

¹ Key Laboratory of Quantum Optics, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China

² Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

³ Wuhan Optics Valley Aerospace Sanjiang Laser Industrial Technology Research Institute Co., Ltd., Wuhan 430075, China

⁴ Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China

Figures & Tables

Fig. 1. (a) Principle scheme of proposed color ghost imaging and (b) its sampling model.

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Fig. 2. Illustration of non-local self-similarity in the spatial domain for a single-wavelength image and in the spectral domain between two wavelength images.

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Fig. 3. Simulation results of three reconstruction algorithms in the case of detection $SNR = 30 dB$ and k $= 10, 000$ . The first row is the object and their corresponding RGB images. The second row is the results of GISC_R. The third row is the results of GISC. The fourth row is the results of GISCNL. (c) The PSNR of reconstruction results in different reconstruction algorithms. The last column is the average processing time.

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Fig. 4. Effect of measurement number k on the reconstruction quality when the detection $SNR = 30 dB$ . The first row is the results of GISC_R. The second row is the results of GISC. The third row is the results of GISCNL. (a)–(f) Corresponding reconstruction results when the measurement number k is 4000, 6000, 8000, 10,000, 12,000, and 14,000, respectively. ( $g$ ) The curve of PSNR versus k.

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Fig. 5. Influence of detection SNR on the reconstruction quality when k $= 10, 000$ . The first row is the results of GISC_R. The second row is the results of GISC. The third row is the results of GISCNL. (a)–(f) Corresponding reconstruction results when the detection SNR is 15 dB, 20 dB, 25 dB, 30 dB, 35 dB, and 40 dB, respectively. (g) The curve of PSNR versus detection SNR.

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Pengwei Wang, Chenglong Wang, Cuiping Yu, Shuai Yue, Wenlin Gong, Shensheng Han. Color ghost imaging via sparsity constraint and non-local self-similarity[J]. Chinese Optics Letters, 2021, 19(2): 021102.

Color ghost imaging via sparsity constraint and non-local self-similarity Download： 809次

Fig. 1. (a) Principle scheme of proposed color ghost imaging and (b) its sampling model.

Fig. 2. Illustration of non-local self-similarity in the spatial domain for a single-wavelength image and in the spectral domain between two wavelength images.

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Color ghost imaging via sparsity constraint and non-local self-similarity Download： 809次

Fig. 1. (a) Principle scheme of proposed color ghost imaging and (b) its sampling model.

Fig. 2. Illustration of non-local self-similarity in the spatial domain for a single-wavelength image and in the spectral domain between two wavelength images.

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