Principles and applications of time-encoded single-pixel imaging technology (Conference Presentation)

Author(s):  
Hongwei Chen
2020 ◽  
Vol 10 (9) ◽  
pp. 3100
Author(s):  
Guang Shi ◽  
Leijue Zheng ◽  
Wen Wang ◽  
Keqing Lu

Existing scanning laser three-dimensional (3D) imaging technology has slow measurement speed. In addition, the measurement accuracy of non-scanning laser 3D imaging technology based on area array detectors is limited by the resolution and response frequency of area array detectors. As a result, applications of laser 3D imaging technology are limited. This paper completed simulations and experiments of a non-scanning 3D imaging system with a single-pixel detector. The single-pixel detector can be used to achieve 3D imaging of a target by compressed sensing to overcome the shortcomings of the existing laser 3D imaging technology. First, the effects of different sampling rates, sparse transform bases, measurement matrices, and reconstruction algorithms on the measurement results were compared through simulation experiments. Second, a non-scanning 3D imaging experimental platform was designed and constructed. Finally, an experiment was performed to compare the effects of different sampling rates and reconstruction algorithms on the reconstruction effect of 3D imaging to obtain a 3D image with a resolution of 8 × 8. The simulation results show that the reconstruction effect of the Hadamard measurement matrix and the minimum total variation reconstruction algorithm performed well.


2017 ◽  
Vol 18 (9) ◽  
pp. 1261-1267 ◽  
Author(s):  
Qiang Guo ◽  
Yu-xi Wang ◽  
Hong-wei Chen ◽  
Ming-hua Chen ◽  
Si-gang Yang ◽  
...  

2013 ◽  
Vol 33 (12) ◽  
pp. 1211007 ◽  
Author(s):  
马彦鹏 Ma Yanpeng ◽  
王亚南 Wang Yanan ◽  
王义坤 Wang Yikun ◽  
葛明锋 Ge Mingfeng ◽  
王雨曦 Wang Yuxi ◽  
...  

2019 ◽  
Author(s):  
Stefan Schimschal ◽  
Stephen Fayers ◽  
Nicklas Ritzmann ◽  
Martin Cox ◽  
Iain Whyte

2019 ◽  
Vol 8 (4) ◽  
pp. 9548-9551

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.


Author(s):  
Detlef Winkelmann ◽  
Manfred Lutz ◽  
Damodar M. Pai ◽  
Andrew R. Melnyk ◽  
Richard Hann ◽  
...  
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