Distorted point spread function and image reconstruction for ghost imaging

Li, Zijie; Zhao, Qing; Gong, Wenlin

doi:10.1016/j.optlaseng.2020.106486

Cited by 15 publications

(6 citation statements)

References 30 publications

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“…To verify the idea, the parameters of the experimental demonstration based on the schematic of Fig. 1 Y up =Y CSPI =Y CSPI correction =Y SPI correction [26] , the value β of SPI up is only 0.06, whereas it approaches 1 for the CSPI correction , which means that the reconstruction image O up SPI x will be very bad, but the target's image can be perfectly recovered by CSPI correction method.…”

Section: Simulated and Experimental Resultsmentioning

confidence: 97%

Enhancing the ability of single-pixel imaging against the source’s energy fluctuation by complementary detection

Cai,

Gong

2024

中国光学快报

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The source's energy fluctuation has a great effect on the quality of single-pixel imaging (SPI). When the method of complementary detection is introduced into an SPI camera system and the echo signal is corrected with the summation of the light intensities recorded by two complementary detectors, we demonstrate, by both experiments and simulations, that complementary single-pixel imaging (CSPI) is robust to the source's energy fluctuation. The superiority of the CSPI structure is also discussed in comparison with previous SPI via signal monitoring.

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Section: Simulated and Experimental Resultsmentioning

confidence: 97%

Enhancing the ability of single-pixel imaging against the source’s energy fluctuation by complementary detection

Cai,

Gong

2024

中国光学快报

Self Cite

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“…, then 𝐼 is the overall transition matrix from the sensor coordinate frame to the ECI coordinate frame. Combined with the results of the image Due to point spread and other reasons, the energy of the target imaging point is dispersed to the surrounding pixels, which affects the accurate pixel coordinates of the target imaging point [39,40]. The target imaging region is divided into rectangular regions.…”

Section: Multi-satellite Cross-positioningmentioning

confidence: 99%

Velocity Estimation for Space Infrared Dim Targets Based on Multi-Satellite Observation and Robust Locally Weighted Regression

Zhang,

Rao,

Zhang

et al. 2023

Remote Sensing

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Velocity estimation of space moving targets is a key part of space situational awareness. However, most of the existing methods do not consider the satellite observation process, and the performance mainly depends on the preset target motion state, which has great limitations. To accurately obtain the motion characteristics of space infrared dim targets in space-based infrared detection, a velocity estimation method based on multi-satellite observation and robust locally weighted regression is proposed. Firstly, according to parameters such as satellite position, satellite attitude angle, and sensor line of sight, the overall target observation model from the sensor coordinate frame to the Earth-centered inertial coordinate frame is established, and the pixel coordinates of the target imaging point are extracted using the gray-weighted centroid method. Then, combined with the least squares criterion, the position sequence of the space target is obtained. Finally, a robust locally weighted regression operation is performed on the target position sequence to estimate the velocity. This study verified the feasibility of the proposed method through simulation examples, with the results showing that the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of the method were only 0.0733 m/s and 1.6640 m/s without measurement error. Moreover, the velocity estimation accuracy was better than that of other methods in most scenarios. In addition, the estimation accuracy under the impact of various measurement errors was analyzed, and it was found that the pixel coordinate extraction error had the greatest impact on velocity estimation accuracy. The proposed method provides a technical basis for the recognition of space infrared dim moving targets.

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“…The PSF measurement system based on the description by Rossmann [34], is shown in figure 3,which is used to measure the PSFs of the five scattering media respectively corresponding to the five networks [7,[35][36][37] so that the impact of scattering media in the projection path on illumination patterns can be mathematically quantified [38].…”

Section: Psf Measurement Systemmentioning

confidence: 99%

Computational ghost imaging with PSF-guiding deep learning through various unknown turbid scattering media

Chen¹,

Xiao²,

Cheng³

et al. 2022

J. Opt.

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Achieving high signal-to-noise ratio (SNR) imaging through scattering media is challenging. Computational ghost imaging with deep learning (CGIDL) has unique advantages for solving this challenge. However, image reconstruction cannot be guaranteed due to low correlation between real signal and training dataset, when the CGIDL is applied in different unknown scattering media. Point spread function (PSF) determines the quality of CGIDL reconstruction, linking the mathematical features of the scene and the quality of reconstruction. In this study, an innovative CGIDL technology based on the measured PSF method is proposed to improve the correlation between real signal and training dataset. When five new turbid scattering media with unknown scattering strength are introduced, classification of PSF enables high SNR imaging through various turbid scattering media.

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Distorted point spread function and image reconstruction for ghost imaging

Cited by 15 publications

References 30 publications

Enhancing the ability of single-pixel imaging against the source’s energy fluctuation by complementary detection

Enhancing the ability of single-pixel imaging against the source’s energy fluctuation by complementary detection

Velocity Estimation for Space Infrared Dim Targets Based on Multi-Satellite Observation and Robust Locally Weighted Regression

Computational ghost imaging with PSF-guiding deep learning through various unknown turbid scattering media

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