Ms. Qianying Cui Profile

Qianying Cui

at Nanjing Univ of Science and Technology

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Proceedings Article | 15 January 2021 Paper

Reconstructing targets based on the enhancement of speckle patterns with the hybrid input-output algorithm

Qianqian Cheng, Enlai Guo, Qianying Cui, Jing Han, Lianfa Bai

Proceedings Volume 11761, 1176119 (2021) https://doi.org/10.1117/12.2586639

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Recovering the object hidden in the disorganized speckle pattern generated through diffusive materials is an important topic as well as a difficult challenge. Existing speckle correlation imaging approaches generally use the speckle autocorrelation to extract the Fourier amplitude information of the target. Our goal here is to research the effects of the quality of the speckle autocorrelation on reconstructing targets via HIO-ER (hybrid input-output and the error reduction) algorithm. Specifically, a low-quality speckle pattern is preprocessed to estimate a high-quality autocorrelation. The PSNR of preprocessed autocorrelations could be increased from 5.88 dB to 24.08 dB. We also compare the differences between the preprocessed and unprocessed methods, and the reconstruction quality could be significantly improved than the later one. The result indicates that a high-quality speckle autocorrelation obtained after preprocessing helps to optimize reconstructing targets

Proceedings Article | 15 January 2021 Paper

High sensitive locating through scattering medium with phase-space-prior

Shuo Zhu, Enlai Guo, Qianying Cui, Dongliang Zheng, Lianfa Bai, Jing Han

Proceedings Volume 11761, 117611W (2021) https://doi.org/10.1117/12.2586521

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The light scattering brings serious degradation for the object information. The conventional optical techniques cannot extract the relevant message on the object location in the scattering. In this paper, in the phase-space, the speckle characteristic with different depths has been analyzed and discussed. We utilize the phase-space-prior to locate the objects through a strong scattering medium with a learning method. Comparing with the single data-driven method, our scheme can help the deep neural network (DNN) to extract the depth information efficiently. The experimental results proved that our method is novel and technically correct with high locating accuracy. Our technique paves the way to a physical-informed DNN in locating and ranging objects through complex scattering media.

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