Detection Model and Correction Method for Quadrant Detector-Based Computational Ghost Imaging System

Quadrant detector (QD) is a widely adopted position sensor. By adopting ghost imaging, the four-channel outputs of the detector can be multiplexed to position and image the target simultaneously. However, the lens defocus and detector blind area distort the detected laser intensities, which would in...

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Bibliographic Details
Published in:IEEE sensors journal Vol. 24; no. 14; pp. 22565 - 22574
Main Authors: Wang, Siyuan, Yu, Zijian, Li, Lijing
Format: Journal Article
Language:English
Published: New York IEEE 15.07.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accuracy
Algorithms
Computational imaging
Detectors
Error analysis
Error correction
Error detection
Ghosts
Image quality
Image reconstruction
Imaging
Laser modes
Lenses
light field modulation
Light spots
Luminous intensity
Measurement by laser beam
neural network (NN)
Neural networks
Noise levels
Position sensing
quadrant detector (QD)
Quadrants
Radar detection
Radar imaging
semiactive laser (SAL) guidance
Sensors
Signal to noise ratio
single-pixel imaging (SPI)
Target detection
ISSN:1530-437X, 1558-1748
Online Access:Get full text
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Summary:Quadrant detector (QD) is a widely adopted position sensor. By adopting ghost imaging, the four-channel outputs of the detector can be multiplexed to position and image the target simultaneously. However, the lens defocus and detector blind area distort the detected laser intensities, which would increase positioning error and reduce reconstruction image quality. In this research, the detection model is set up and analyzed based on the light spot distribution and detector characteristics. A neural network (NN)-based fitting method is proposed to directly predict spot position and correct total light intensities with detector outputs. The network is constructed and trained with simulation data. The prediction accuracy and generalization performance are verified by numerical simulations. The effectiveness of the proposed method is demonstrated in the experimental system. The positioning error of the proposed method decreases by 98.0% compared to the classic algorithm, and the signal-to-noise ratio of the reconstructed image increases by 31.94 dB. The proposed scheme has the potential to improve positioning accuracy and imaging quality of QD-based detection systems, such as radar and guidance applications.
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content type line 14
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2024.3394171