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Title: Needle-based deep-neural-network camera

We experimentally demonstrate a camera whose primary optic is a cannula/needle (diameter=0.22mmandlength=12.5mm) that acts as a light pipe transporting light intensity from an object plane (35 cm away) to its opposite end. Deep neural networks (DNNs) are used to reconstruct color and grayscale images with a field of view of 18° and angular resolution of∼<#comment/>0.4∘<#comment/>. We showed a large effective demagnification of127×<#comment/>. Most interestingly, we showed that such a camera could achieve close to diffraction-limited performance with an effective numerical aperture of 0.045, depth of focus∼<#comment/>16µ<#comment/>m, and resolution close to the sensor pixel size (3.2 µm). When trained on images with depth information, the DNN can create depth maps. Finally, we show DNN-based classification of the EMNIST dataset before and after image reconstructions. The former could be useful for imaging with enhanced privacy.

 
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NSF-PAR ID:
10217917
Author(s) / Creator(s):
; ;
Publisher / Repository:
Optical Society of America
Date Published:
Journal Name:
Applied Optics
Volume:
60
Issue:
10
ISSN:
1559-128X; APOPAI
Page Range / eLocation ID:
Article No. B135
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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