scholarly journals X-ray computational ghost imaging with single-pixel detector

2019 ◽  
Vol 27 (3) ◽  
pp. 3284 ◽  
Author(s):  
Y. Klein ◽  
A. Schori ◽  
I. P. Dolbnya ◽  
K. Sawhney ◽  
S. Shwartz
Author(s):  
Y. Klein ◽  
A. Schori ◽  
I. P. Dolbnya ◽  
K. Sawhney ◽  
S. Shwartz

2020 ◽  
Vol 28 (17) ◽  
pp. 24568
Author(s):  
O. Sefi ◽  
Y. Klein ◽  
E. Strizhevsky ◽  
I. P. Dolbnya ◽  
S. Shwartz

Author(s):  
Yu-Hang He ◽  
Ai-Xin Zhang ◽  
Ming-Fei Li ◽  
Yi-Yi Huang ◽  
Bao-Gang Quan ◽  
...  

Author(s):  
Yasuhiro Mizutani ◽  
Shoma Kataoka ◽  
Tsutomu Uenohara ◽  
Yasuhiro Takaya

AbstractWe propose ghost imaging (GI) with deep learning to improve detection speed. GI, which uses an illumination light with random patterns and a single-pixel detector, is correlation-based and thus suitable for detecting weak light. However, its detection time is too long for practical inspection. To overcome this problem, we applied a convolutional neural network that was constructed based on a classification of the causes of ghost image degradation. A feasibility experiment showed that when using a digital mirror device projector and a photodiode, the proposed method improved the quality of ghost images.


2015 ◽  
Vol 45 (1) ◽  
pp. 92-98 ◽  
Author(s):  
Sheng Yuan ◽  
Xuemei Liu ◽  
Xin Zhou ◽  
Zhongyang Li ◽  
Yangrui Yang

Optik ◽  
2017 ◽  
Vol 147 ◽  
pp. 136-142 ◽  
Author(s):  
Bin Bai ◽  
Yuchen He ◽  
Jianbin Liu ◽  
Yu Zhou ◽  
Huaibin Zheng ◽  
...  

Science ◽  
2018 ◽  
Vol 360 (6394) ◽  
pp. 1246-1251 ◽  
Author(s):  
Sadao Ota ◽  
Ryoichi Horisaki ◽  
Yoko Kawamura ◽  
Masashi Ugawa ◽  
Issei Sato ◽  
...  

Ghost imaging is a technique used to produce an object’s image without using a spatially resolving detector. Here we develop a technique we term “ghost cytometry,” an image-free ultrafast fluorescence “imaging” cytometry based on a single-pixel detector. Spatial information obtained from the motion of cells relative to a static randomly patterned optical structure is compressively converted into signals that arrive sequentially at a single-pixel detector. Combinatorial use of the temporal waveform with the intensity distribution of the random pattern allows us to computationally reconstruct cell morphology. More importantly, we show that applying machine-learning methods directly on the compressed waveforms without image reconstruction enables efficient image-free morphology-based cytometry. Despite a compact and inexpensive instrumentation, image-free ghost cytometry achieves accurate and high-throughput cell classification and selective sorting on the basis of cell morphology without a specific biomarker, both of which have been challenging to accomplish using conventional flow cytometers.


APL Photonics ◽  
2020 ◽  
Vol 5 (5) ◽  
pp. 056102 ◽  
Author(s):  
Yu-Hang He ◽  
Ai-Xin Zhang ◽  
Ming-Fei Li ◽  
Yi-Yi Huang ◽  
Bao-Gang Quan ◽  
...  

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 732 ◽  
Author(s):  
Ming-Jie Sun ◽  
Jia-Min Zhang

Whereas modern digital cameras use a pixelated detector array to capture images, single-pixel imaging reconstructs images by sampling a scene with a series of masks and associating the knowledge of these masks with the corresponding intensity measured with a single-pixel detector. Though not performing as well as digital cameras in conventional visible imaging, single-pixel imaging has been demonstrated to be advantageous in unconventional applications, such as multi-wavelength imaging, terahertz imaging, X-ray imaging, and three-dimensional imaging. The developments and working principles of single-pixel imaging are reviewed, a mathematical interpretation is given, and the key elements are analyzed. The research works of three-dimensional single-pixel imaging and their potential applications are further reviewed and discussed.


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