论文标题
基于Y-NET的幽灵成像：动态编码和结合编码方法
Ghost imaging based on Y-net: a dynamic coding and conjugate-decoding approach
论文作者
论文摘要
融合深度学习技术的幽灵成像最近在光学成像领域引起了很多关注。但是，在大多数情况下，确定性的照明和多次暴露仍然是必不可少的。在这里，我们提出了一个基于新颖的共轭深度学习框架（Y-net）的幽灵成像方案，该框架在确定性和不确定的照明下都很好地工作。从我们网络的端到端特征中受益，可以直接从检测器收集的一对相关斑点中实现样品的图像，并且在实验中仅照亮了一次样品。只要斑点的统计特征保持不变，编码实验中样品的斑点的空间分布可能与训练的模拟斑点完全不同。这种方法对于高分辨率X射线幽灵成像应用程序尤为重要，因为它有可能提高图像质量和减少辐射损伤。共轭网络的想法也可以应用于其他基于学习的成像
Ghost imaging incorporating deep learning technology has recently attracted much attention in the optical imaging field. However, deterministic illumination and multiple exposure are still essential in most scenarios. Here we propose a ghost imaging scheme based on a novel conjugate-decoding deep learning framework (Y-net), which works well under both deterministic and indeterministic illumination. Benefited from the end-to-end characteristic of our network, the image of a sample can be achieved directly from a pair of correlated speckles collected by the detectors, and the sample is illuminated only once in the experiment. The spatial distribution of the speckles encoding the sample in the experiment can be completely different from that of the simulation speckles for training, as long as the statistical characteristics of the speckles remain unchanged. This approach is particularly important to high-resolution x-ray ghost imaging applications due to its potential for improving image quality and reducing radiation damage. And the idea of conjugate-decoding network may also be applied to other learning-based imaging
