Speckle-Shifting Ghost Imaging

Mao, Tianyi; Chen, Qian; He, Weiji; Zou, Yunhao; Dai, Honghua; Gu, Guohua

doi:10.1109/jphot.2016.2578934

Cited by 31 publications

(22 citation statements)

References 29 publications

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“…Recent results [25] have demonstrated the scaling laws for the achievable contrast of the retrieved ghost images, which strongly depend on the ratio between object size and the speckle size of the pseudothermal light for the same number of independent iterations. There are also various approaches focused on improving the imaging quality by using computational methods, e.g., sparsity constraints [26,27], THz patterns [28,29], differential ghost imaging [30], and computational ghost imaging by using special patterns [31][32][33][34][35]. Based on these studies, the improvement of pseudothermal ghost imaging quality has been greatly promoted.…”

Section: Introductionmentioning

confidence: 99%

Improving the Contrast of Pseudothermal Ghost Images Based on the Measured Signal Distribution of Speckle Fields

2021

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In this study, we examine the quality of microscale ghost images as a function of the measured histographic signal distribution of the speckle fields from a nonuniform pseudothermal light source. This research shows that the distribution of the detected signal level on each pixel of the camera plays a significant role in improving the contrast-to-noise ratio (CNR) of pseudothermal ghost imaging. To our knowledge, the scaling of CNR with different pixel intensity distributions of the speckle fields is observed for the first time in the field of pseudothermal microscale ghost imaging. The experimental observations are in very good agreement with numerical analysis. Based on these findings, we can predict the settings for light sources that will maximize the CNR of microscale ghost images.

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Section: Introductionmentioning

confidence: 99%

Improving the Contrast of Pseudothermal Ghost Images Based on the Measured Signal Distribution of Speckle Fields

2021

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“…GI offers great promise for its higher spatial resolution [3], [4] and higher detection sensitivity [5]. Thus, the past years have witnessed a rapidly growing interest in the applications of GI, ranging from laser radars [6], [7], microscopes [8], [9], object edges extraction [10]- [13], image hiding methods [14]- [16] to optical encryption schemes [17]- [20].…”

Section: Introductionmentioning

confidence: 99%

Multiple-Input Single-Output Ghost Imaging

Wang

Zhao

2020

IEEE Photonics J.

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In this paper, we propose a multiple-input single-output ghost imaging scheme, named MISO-GI. In the scheme, K spatiotemporal speckle pattern sources (multiple input) simultaneously generate K different spatiotemporal modulated speckle patterns which then are illuminated on an unknown object at the same time. Then, a non-spatial-resolution bucket detector (single output) is used to detect the light field interacting with the object. According to the orthogonality of speckle patterns from different spatiotemporal speckle pattern sources, the detection signal is separated into K components corresponding to K spatiotemporal modulated speckle patterns. With the effective recovery algorithm, an image is reconstructed by using all spatiotemporal modulated speckle patterns and their corresponding components of detection signals. Both experimental and simulation results verify the feasibility of the proposed MISO-GI scheme. Moreover, the results also show that the proposed MISO-GI scheme could reduce the total sampling time by K times and improve the imaging quality comparing to traditional GI, without the decrease of the number of speckle patterns and the picture time as well as the consumption of other light field dimensions. It provides a promising avenue to realize GI with limited sampling time and has potential for more applications.

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“…Liu et al proposed a gradient GI (GGI) scheme which directly achieved the edge information of an unknown target object [27]. Subsequently, a more optimized edge detection method named speckle-shifting GI (SSGI) was reported by Mao [28]. The SSGI scheme does't need the gradient angle or any other prior knowledge of the object in GGI.…”

Section: Introductionmentioning

confidence: 99%

Edge detection based on joint iteration ghost imaging

et al. 2019

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Imaging and edge detection have been widely applied and played an important role in security checking and medical diagnosis. However, as we know, most edge detection based on ghost imaging system require a large measurement times and the target object image cannot be provided directly. In this work, a new edge detection based on joint iteration of projected Landweber iteration regularization and guided filter ghost imaging method have been proposed which can be improved the feature detection quality in ghost imaging. This method can also achieve high quality imaging. Simulation and experiment results show that the spatial information and edge information of target object are successfully recovered from the random speckle patterns without special coding under a low measurement times, and the edge image quality is improved remarkably. This approach improves the the applicability of ghost imaging, and can satisfy the practical application fields of imaging and edge detection at the same time.

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Speckle-Shifting Ghost Imaging

Cited by 31 publications

References 29 publications

Improving the Contrast of Pseudothermal Ghost Images Based on the Measured Signal Distribution of Speckle Fields

Improving the Contrast of Pseudothermal Ghost Images Based on the Measured Signal Distribution of Speckle Fields

Multiple-Input Single-Output Ghost Imaging

Edge detection based on joint iteration ghost imaging

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