McEvoy Luke, Tafone Daniel, Sua Yong Meng, Huang Yuping
Department of Physics, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, NJ, 07030, USA.
Center for Quantum Science and Engineering, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, NJ, 07030, USA.
Sci Rep. 2024 Aug 29;14(1):20078. doi: 10.1038/s41598-024-71095-x.
Imaging technology based on detecting individual photons has seen tremendous progress in recent years, with broad applications in autonomous driving, biomedical imaging, astronomical observation, and more. Comparing with conventional methods, however, it takes much longer time and relies on sparse and noisy photon-counting data to form an image. Here we introduce Physics-Informed Masked Autoencoder (PI-MAE) as a fast and efficient approach for data acquisition and image reconstruction through hardware implementation of the MAE (Masked Autoencoder). We examine its performance on a single-photon LiDAR system when trained on digitally masked MNIST data. Our results show that, with or less detected photons per pulse and down to 9 detected photons per pixel, it achieves high-quality image reconstruction on unseen object classes with 90% physical masking. Our results highlight PI-MAE as a viable hardware accelerator for significantly improving the performance of single-photon imaging systems in photon-starving applications.
近年来，基于检测单个光子的成像技术取得了巨大进展，在自动驾驶、生物医学成像、天文观测等领域有着广泛应用。然而，与传统方法相比，它需要更长的时间，并且依赖稀疏且有噪声的光子计数数据来形成图像。在此，我们引入物理感知掩码自动编码器（PI-MAE），作为一种通过掩码自动编码器（MAE）的硬件实现来进行数据采集和图像重建的快速有效方法。我们在对数字掩码MNIST数据进行训练时，研究了其在单光子激光雷达系统上的性能。我们的结果表明，在每个脉冲检测到10个或更少光子且每个像素低至9个检测光子的情况下，它能在90%物理掩码的未见物体类别上实现高质量图像重建。我们的结果突出了PI-MAE作为一种可行的硬件加速器，可显著提高单光子成像系统在光子匮乏应用中的性能。
