Single-point material recognition by quantum parametric mode sorting and photon counting.

We explore an active illumination approach to remote material recognition, based on quantum parametric mode sorting and single-photon detection. By measuring a photon's time of flight at picosecond resolution, 97.8% recognition is demonstrated by illuminating only a single point on the materials. Thanks to the exceptional detection sensitivity and noise rejection, a high recognition accuracy of 96.1% is achieved even when the materials are occluded by a lossy and multiscattering obscurant.

Robust and efficient single-pixel image classification with nonlinear optics.

We present a hybrid image classifier by feature-sensitive image upconversion, single pixel photodetection, and deep learning, aiming at fast processing of high-resolution images. It uses partial Fourier transform to extract the images' signature features in both the original and Fourier domains, thereby significantly increasing the classification accuracy and robustness. Tested on the Modified National Institute of Standards and Technology handwritten digit images and verified by simulation, it boosts accuracy from 81.25% (by Fourier-domain processing) to 99.23%, and achieves 83% accuracy for highly contaminated images whose signal-to-noise ratio is only -17dB. Our approach could prove useful for fast lidar data processing, high-resolution image recognition, occluded target identification, and atmosphere monitoring.

Single-pixel pattern recognition with coherent nonlinear optics.

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.