Ghost Imaging of Moving Target Based on the Periodic Pseudo-Thermal Light Field Generated by a 2D Silicon OPA

Wang, Zi-Hao; Cao, Rui; Liao, Jiali; Sun, Yanjun; Lu, Y. L.; Li, Jin; Feng, Junbo; Cao, Changqing

doi:10.1109/jphot.2022.3145000

Cited by 4 publications

(1 citation statement)

References 43 publications

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“…It simplifies the light path and improves the performance of ghost imaging. In comparison to traditional imaging, CGI has already prohibited great potentials in applications, such as single-pixel imaging [5,6], biological imaging [7][8][9][10], information encryption [11][12][13], laser radar technique [14,15] and imaging through atmospheric turbulence environment [16][17][18][19][20][21]. However, the reflectivity or transmittance of targets are sometimes close to the background in many real-world scenarios, making it difficult to identify the targets from the background by the received light intensity.…”

Section: Introductionmentioning

confidence: 99%

High-performance deep-learning based polarization computational ghost imaging with random patterns and orthonormalization

Fan

et al. 2023

Phys. Scr.

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Polarization computational ghost imaging (PCGI) often requires a large number of samples to reconstruct the targets, which can be optimized by reducing sampling rates with the aids of deep-learning technology. In this paper, the random patterns and successive orthonormalization instead of common Hadamard patterns, has been introduced into the deep-learning based PCGI system to recover high-quality images at lower sampling rates. Firstly, we use a polarized light to illuminate the target with random patterns for sampling. Then we can obtain a vector of bucket detector values containing the reflective information of the target. Secondly, we orthonormalize the vector according to the random patterns. Subsequently, the orthonormalized data can be input into the Improved U-net (IU-net) for reconstructing the targets. We demonstrate that higher-quality image of the testing sample can be obtained at a lower sampling rate of 1.5%, and superior-generalization ability for the untrained complex targets can be also achieved at a lower sampling rate of 6%. Meanwhile, we have also investigated the generalization ability of the system for the untrained targets with different materials that have different depolarization properties, and the system still demonstrates superior performances. The proposed method may pave a way towards the real applications of the PCGI.

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Section: Introductionmentioning

confidence: 99%

High-performance deep-learning based polarization computational ghost imaging with random patterns and orthonormalization

Fan

et al. 2023

Phys. Scr.

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A review of ToF-based LiDAR

Ma,

Zhuo,

Qiu

et al. 2024

J. Semicond.

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In recent years, propelled by the rapid iterative advancements in digital imaging technology and the semiconductor industry, encompassing microelectronic design, manufacturing, packaging, and testing, time-of-flight (ToF)-based imaging systems for acquiring depth information have garnered considerable attention from both academia and industry. This technology has emerged as a focal point of research within the realm of 3D imaging. Owing to its relatively straightforward principles and exceptional performance, ToF technology finds extensive applications across various domains including human−computer interaction, autonomous driving, industrial inspection, medical and healthcare, augmented reality, smart homes, and 3D reconstruction, among others. Notably, the increasing maturity of ToF-based LiDAR systems is evident in current developments. This paper comprehensively reviews the fundamental principles of ToF technology and LiDAR systems, alongside recent research advancements. It elucidates the innovative aspects and technical challenges encountered in both transmitter (TX) and receiver (RX), providing detailed discussions on corresponding solutions. Furthermore, the paper explores prospective avenues for future research, offering valuable insights for subsequent investigations.

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Image Reconstruction of Targets Undergoing Complex Motion Based on Optical Phased Array Compressed-Sensing Ghost Imaging Technology

Wang

Sun

Liao

et al. 2022

J. Lightwave Technol.

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Ghost Imaging of Moving Target Based on the Periodic Pseudo-Thermal Light Field Generated by a 2D Silicon OPA

Cited by 4 publications

References 43 publications

High-performance deep-learning based polarization computational ghost imaging with random patterns and orthonormalization

High-performance deep-learning based polarization computational ghost imaging with random patterns and orthonormalization

A review of ToF-based LiDAR

Image Reconstruction of Targets Undergoing Complex Motion Based on Optical Phased Array Compressed-Sensing Ghost Imaging Technology

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