Influence of the source’s energy fluctuation on computational ghost imaging and effective correction approaches

Xiaodong Mei; Chenglong Wang; Yami Fang; Ting Song; Wenlin Gong; Shensheng Han

doi:doi:10.3788/COL202018.042602

Chinese Optics Letters, 2020, 18 (4): 042602, Published Online: Apr. 15, 2020

Influence of the source’s energy fluctuation on computational ghost imaging and effective correction approaches Download： 738次

Xiaodong Mei ^1,2Chenglong Wang ¹Yami Fang ³Ting Song ³Wenlin Gong ^1,*Shensheng Han ¹

Author Affiliations

¹ Key Laboratory for Quantum Optics and Center for Cold Atom Physics of CAS, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China

² University of Chinese Academy of Sciences, Beijing 100049, China

³ Shanghai Key Laboratory of Aerospace Intelligent Control Technology, Shanghai Aerospace Control Technology Institute, Shanghai 201800, China

Figures & Tables

Fig. 1. Schematics of the (a) standard CGI and (b) CGI with A or Y Correction.

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Fig. 2. Proof-of-principle experimental setup of the CGI with A or Y Correction for an unstable source.

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Fig. 3. Simulative and experimental results of imaging a reflection gray object (four letters “SIOM”) when the RMS of the laser’s energy fluctuation is 0%, 2%, 4%, 6%, 8%, and 10%, respectively. (I) Simulative results and (II) experimental results. (a) CGI with No Correction, (b) CGI with A Correction, (c) CGI with Y Correction, (d) CGISC with No Correction, (e) CGISC with A Correction, and (f) CGISC with Y Correction.

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Fig. 4. Relationship between SSIM and RMS of the laser’s energy fluctuation, based on the simulation and experimental results shown in Fig. 3. (a) The SSIM-RMS curves of the simulation results, and (b) the SSIM-RMS curves of the experimental results.

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Fig. 5. $M_{C}$ matrices for different energy fluctuations. The RMS of energy fluctuation of each matrix is 0% to 10% from (a) to (f).

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Fig. 6. Experimental demonstration result based on our existing CGI lidar system. (a) The histogram of the source’s energy fluctuation and its corresponding Gaussian fitting, (b) the target, (c) and (e) are the reconstruction results of CGI and CGISC with No Correction, and (d) and (f) are the corresponding reconstruction results of CGI and CGISC with Y Correction.

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Xiaodong Mei, Chenglong Wang, Yami Fang, Ting Song, Wenlin Gong, Shensheng Han. Influence of the source’s energy fluctuation on computational ghost imaging and effective correction approaches[J]. Chinese Optics Letters, 2020, 18(4): 042602.

Influence of the source’s energy fluctuation on computational ghost imaging and effective correction approaches Download： 738次

Fig. 1. Schematics of the (a) standard CGI and (b) CGI with A or Y Correction.

Fig. 2. Proof-of-principle experimental setup of the CGI with A or Y Correction for an unstable source.

Fig. 4. Relationship between SSIM and RMS of the laser’s energy fluctuation, based on the simulation and experimental results shown in Fig. 3. (a) The SSIM-RMS curves of the simulation results, and (b) the SSIM-RMS curves of the experimental results.

Fig. 5. $M_{C}$ matrices for different energy fluctuations. The RMS of energy fluctuation of each matrix is 0% to 10% from (a) to (f).

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Influence of the source’s energy fluctuation on computational ghost imaging and effective correction approaches Download： 738次

Fig. 1. Schematics of the (a) standard CGI and (b) CGI with A or Y Correction.

Fig. 2. Proof-of-principle experimental setup of the CGI with A or Y Correction for an unstable source.

Fig. 4. Relationship between SSIM and RMS of the laser’s energy fluctuation, based on the simulation and experimental results shown in Fig. 3. (a) The SSIM-RMS curves of the simulation results, and (b) the SSIM-RMS curves of the experimental results.

Fig. 5. MC matrices for different energy fluctuations. The RMS of energy fluctuation of each matrix is 0% to 10% from (a) to (f).

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Fig. 5. $M_{C}$ matrices for different energy fluctuations. The RMS of energy fluctuation of each matrix is 0% to 10% from (a) to (f).