High-efficiency single-photon compressed sensing imaging based on the best choice scheme

With single-photon sensitivity and picosecond resolution, single-photon imaging technology is an ideal solution for extreme conditions and ultra-long distance imaging. However, the current single-photon imaging technology has the problem of slow imaging speed and poor quality caused by the quantum shot noise and the fluctuation of background noise. In this work, an efficient single-photon compressed sensing imaging scheme is proposed, in which a new mask is designed by the Principal Component Analysis algorithm and the Bit-plane Decomposition algorithm. By considering the effects of quantum shot noise, dark count on imaging, the number of masks is optimized to ensure high-quality single-photon compressed sensing imaging with different average photon counts. The imaging speed and quality are greatly improved compared with the commonly used Hadamard scheme. In the experiment, a 64 x 64 pixels' image is obtained with only 50 masks, the sampling compression rate reaches 1.22%, and the sampling speed increases by 81 times. The simulation and experimental results demonstrated that the proposed scheme will effectively promote the application of single-photon imaging in practical scenarios.