Moodley Chané, Sephton Bereneice, Rodríguez-Fajardo Valeria, Forbes Andrew
School of Physics, University of the Witwatersrand, Johannesburg, 2000, South Africa.
Sci Rep. 2021 Apr 20;11(1):8561. doi: 10.1038/s41598-021-88197-5.
Quantum ghost imaging offers many advantages over classical imaging, including the ability to probe an object with one wavelength and record the image with another (non-degenerate ghost imaging), but suffers from slow image reconstruction due to sparsity and probabilistic arrival positions of photons. Here, we propose a two-step deep learning approach to establish an optimal early stopping point based on object recognition, even for sparsely filled images. In step one we enhance the reconstructed image after every measurement by a deep convolutional auto-encoder, followed by step two in which a classifier is used to recognise the image. We test this approach on a non-degenerate ghost imaging setup while varying physical parameters such as the mask type and resolution. We achieved a fivefold decrease in image acquisition time at a recognition confidence of [Formula: see text]. The significant reduction in experimental running time is an important step towards real-time ghost imaging, as well as object recognition with few photons, e.g., in the detection of light sensitive structures.
量子鬼成像相对于传统成像具有许多优势，包括能够用一种波长探测物体并用另一种波长记录图像（非简并鬼成像），但由于光子的稀疏性和概率到达位置，其图像重建速度较慢。在此，我们提出一种两步深度学习方法，即使对于稀疏填充的图像，也能基于目标识别建立最佳提前停止点。第一步，我们通过深度卷积自动编码器在每次测量后增强重建图像，第二步使用分类器识别图像。我们在非简并鬼成像设置上测试了这种方法，同时改变诸如掩模类型和分辨率等物理参数。在识别置信度为[公式：见原文]时，我们将图像采集时间减少了五倍。实验运行时间的显著减少是迈向实时鬼成像以及利用少量光子进行目标识别（例如在检测光敏结构中）的重要一步。
