Single-pixel depth imaging

Abstract

The conventional single-pixel imaging (SPI) is unable to directly obtain the target's depth information due to the lack of depth modulation and corresponding decoding. The existing SPI-based depth imaging systems utilize multiple single-pixel detectors to capture multi-angle images, or introduce depth modulation devices such as optical grating to achieve three-dimensional imaging. The methods require bulky systems and high computational complexity. In this paper, we present a novel and efficient three-dimensional SPI method that does not require any additional hardware compared to the conventional SPI system. Specifically, a multiplexing illumination strategy combining random and sinusoidal pattern is proposed, which is able to simultaneously encode the target's spatial and depth information into a measurement sequence captured by a single-pixel detector. To decode the three-dimensional information from one-dimensional measurements, we built and trained a deep convolutional neural network. The end-to-end framework largely accelerates reconstruction speed, reduces computational complexity and improves reconstruction precision. Both simulations and experiments validate the method's effectiveness and efficiency for depth imaging.