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Single-pixel pattern recognition with coherent nonlinear optics
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DOI：10.1364/OL.411564.png)
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Abstract
En 中文
In this Letter, we propose and experimentally demonstrate a nonlinear-optics approach to pattern recognition with single-pixel imaging and a deep neural network. It employs mode-selective image up-conversion to project a raw image onto a set of coherent spatial modes, whereby its signature features are extracted optically in a nonlinear manner. With 40 projection modes, the classification accuracy reaches a high value of 99.49% for the Modified National Institute of Standards and Technology handwritten digit images, and up to 95.32%, even when they are mixed with strong noise. Our experiment harnesses rich coherent processes in nonlinear optics for efficient machine learning, with potential applications in online classification of large-size images, fast lidar data analyses, complex pattern recognition, and so on. (C) 2020 Optical Society of America
Keywords:
DEEP LEARNING RECONSTRUCTION
NEURAL-NETWORKS
ULTRASHORT PULSES
LIGHT