Opt Express. 2023 Feb 27;31(5):7589-7598. doi: 10.1364/OE.481042.
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 × 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.
单光子灵敏度和皮秒分辨率，单光子成像技术是极端条件和超长距离成像的理想解决方案。然而，目前的单光子成像技术存在成像速度慢、质量差的问题，这是由量子散粒噪声和背景噪声波动引起的。在这项工作中，提出了一种高效的单光子压缩感知成像方案，其中通过主成分分析算法和位平面分解算法设计了一种新的掩模。通过考虑量子散粒噪声、成像暗计数的影响，优化了掩模的数量，以确保在不同的平均光子数下实现高质量的单光子压缩感知成像。与常用的 Hadamard 方案相比，成像速度和质量得到了大大提高。在实验中，仅使用 50 个掩模即可获得 64×64 像素的图像，采样压缩率达到 1.22%，采样速度提高了 81 倍。模拟和实验结果表明，所提出的方案将有效地促进单光子成像在实际场景中的应用。
