论文标题
小组框架神经网络移动对象幽灵成像与框架合并算法结合
Group frame neural network of moving object ghost imaging combined with frame merging algorithm
论文作者
论文摘要
提取相关信息的多个样本的性质限制了移动对象的幽灵成像的应用。提出了一种新型的多到一体神经网络，并引入了“批处理框架”的概念以改善串行成像方法。神经网络从少数样品中提取更多相关信息，从而降低了幽灵成像技术的采样比。我们将图像之间的相关特性结合在一起，以提出合并算法的框架，从而消除了高速移动对象的动态模糊，并进一步提高了以低采样比的方式改善移动对象图像的重建质量。实验结果与仿真结果一致。
The nature of multiple samples to extract correlation information limits the applications of ghost imaging of moving objects. A novel multi-to-one neural network is proposed and the concept of "batch frame" is introduced to improve the serial imaging method. The neural network extracts more correlation information from a small number of samples, thus reducing the sampling ratio of the ghost imaging technique. We combine the correlation characteristics between images to propose a frame merging algorithm, which eliminates the dynamic blur of high-speed moving objects and further improves the reconstruction quality of moving object images at a low sampling ratio. The experimental results are consistent with the simulation results.
