Open AccessOptics-free imaging of complex, non-sparse and color QR-codes with deep neural networks
Author(s) -
Soren Nelson,
Evan Scullion,
Rajesh Me
Publication year - 2020
Publication title -
osa continuum
Language(s) - English
Resource type - Journals
ISSN - 2578-7519
DOI - 10.1364/osac.403295
Subject(s) - robustness (evolution) , artificial intelligence , computer science , computer vision , artificial neural network , monochrome , code (set theory) , image sensor , visualization , optics , physics , biochemistry , chemistry , set (abstract data type) , gene , programming language
We demonstrate optics-free imaging of complex color and monochrome QR-codes using a bare image sensor and trained artificial neural networks (ANNs). The ANN is trained to interpret the raw sensor data for human visualization. The image sensor is placed at a specified gap (1mm, 5mm and 10mm) from the QR code. We studied the robustness of our approach by experimentally testing the output of the ANNs with system perturbations of this gap, and the translational and rotational alignments of the QR code to the image sensor. Our demonstration opens us the possibility of using completely optics-free, non-anthropocentric cameras for application-specific imaging of complex, non-sparse objects.