论文标题
自我发展的幽灵成像
Self-evolving ghost imaging
论文作者
论文摘要
幽灵成像可以用点检测器而不是数组传感器捕获2D图像。因此，它为波段中的建筑区域格式传感器的挑战提供了解决方案，因为高性能单像素探测器，这些传感器难以且昂贵，并打开新的成像方式。传统上，幽灵成像通过将测量的光强度和施加照明模式关联来检索对象离线的图像。在这里，我们提出了一种基于反馈的方法，用于在线更新成像结果，该方法可以绕过后处理，称为自我发展的幽灵成像（SEGI）。我们引入了一种遗传算法，以实时优化照明模式，以根据测得的总光强度匹配对象形状。我们从理论上和实验上证明了静态和动态成像的概念。这种方法为诸如遥感（例如机器视觉，自动驾驶汽车中的激光镜系统）和生物成像等应用中实时幽灵成像的新观点打开了新观点。
Ghost imaging can capture 2D images with a point detector instead of an array sensor. It therefore offers a solution to the challenge of building area format sensors in wavebands where such sensors are difficult and expensive to produce and opens up new imaging modalities due to high-performance single-pixel detectors. Traditionally, ghost imaging retrieves the image of an object offline, by correlating measured light intensities and applied illuminating patterns. Here we present a feedback-based approach for online updating of the imaging result that can bypass post-processing, termed self-evolving ghost imaging (SEGI). We introduce a genetic algorithm to optimize the illumination patterns in real-time to match the objects shape according to the measured total light intensity. We theoretically and experimentally demonstrate this concept for static and dynamic imaging. This method opens new perspectives for real-time ghost imaging in applications such as remote sensing (e.g. machine vision, LiDAR systems in autonomous vehicles) and biological imaging.
