Optimization of computational ghost imaging

Gao, Chao; Wang, Xiaoqian; Wang, Zhifeng; Li, Zhe; Du, Guijiao; Chang, Fi‐John; Yao, Zhihai

doi:10.1103/physreva.96.023838

Cited by 20 publications

(12 citation statements)

References 34 publications

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“…5(n)-(s), the qualities of subtracted ghost images in conditional-averaging GI, HGI, and EGI, have been improved. This phenomenon has been reported and discussed in conditional-averaging GI [10,[32][33][34][35][36][37][38]. Different from conditional-averaging GI, this normalized subtraction operation in HGI and EGI effectively suppress the noise in their divergence reconstruction calculations, which therefore increases the image quality.…”

Section: Gi Algorithmsmentioning

confidence: 91%

“…Based on Eq. (3), in order to achieve positive and negative ghost images, the bucket object signals are divided into two sets [10,[32][33][34][35][36][37][38]: for Io i satisfying Io i ≥ Io, Eq. (3) provides a positive ghost image; for Io i satisfying Io i < Io, a negative ghost image appears.…”

Section: Theoretical and Simulated Analysis On Positive And Negative Ghost Imagingmentioning

confidence: 99%

“…Apart from improving GI efficiency, different GI algorithms also significantly affect the type of a recovered ghost image. For example, a conditional-averaging GI algorithm would generate either a positive or negative ghost image [10,[32][33][34][35][36][37][38]. Nevertheless, the imaging reconstruction algorithm effect on positive and negative GI have not been systematically analysed so far, least of all for related potential applications.…”

Section: Introductionmentioning

confidence: 99%

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Positive and Negative Ghost Imaging

et al. 2019

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We study the reconstruction algorithm effect on the positive and negative ghost imaging (GI). By introducing three GI algorithms (logarithmic GI, exponential GI and trigonometric GI), we perform numerical simulations and experiments with different GI algorithms, which demonstrate that a positive or negative ghost image can be recovered by modulating the monotonicity of the bucket object signal function. Furthermore, we propose a positive-negative optical encryption scheme based on trigonometric GI. Our work not only broadens the positive-negative GI principle but also paves a way to its applications in optical encryption.

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Section: Gi Algorithmsmentioning

confidence: 91%

Section: Theoretical and Simulated Analysis On Positive And Negative Ghost Imagingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Positive and Negative Ghost Imaging

et al. 2019

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“…Ghost imaging (GI) is an imaging technique based on secondorder intensity correlation [1][2][3][4][5][6][7][8][9][10] . GI has many advantages compared to traditional imaging techniques.…”

Section: Introductionmentioning

confidence: 99%

Second-order cumulants ghost imaging

et al. 2022

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Ghost imaging (GI) is a technique to retrieve images by correlating intensity fluctuations. In this Letter, we present a novel scheme for GI referred to as second-order cumulants GI (SCGI). The image is retrieved from fluctuation information, and resolution may be enhanced compared to traditional GI. We experimentally performed SCGI image reconstruction, and the results are in agreement with theoretical predictions.

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“…Ghost imaging (GI) is an imaging technique based on correlation measurement [1][2][3][4][5][6][7][8][9][10] . The GI experiment has been demonstrated by Pittman et al 1 .…”

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confidence: 99%