论文标题
具有主动采样模式和基于学习的重建的3D单金成像
3D Single-pixel imaging with active sampling patterns and learning based reconstruction
论文作者
论文摘要
单像素成像（SPI）对于受透射带宽或照明带约束的应用很重要，可以通过捕获信号携带深度来进一步实现3D SPI。采样策略和重建算法是SPI的关键问题。传统上，采样通常采用随机模式，但是这种盲目的被动策略需要高采样率，即使如此，也很难开发出可以保持更高准确性和鲁棒性的重建算法。在本文中，提出了一种主动策略，以通过设计模式进行针对性扫描进行采样，从中可以很好地对空间信息进行很好的重新排序。然后，进一步引入深度学习方法以实现3D重建，并分析了深度学习在低采样率下重建所需信息的能力。大量实验验证我们的方法是否提高了SPI的精度，即使采样率非常低，这有可能根据实际需求在相似的系统中灵活扩展。
Single-pixel imaging (SPI) is significant for applications constrained by transmission bandwidth or lighting band, where 3D SPI can be further realized through capturing signals carrying depth. Sampling strategy and reconstruction algorithm are the key issues of SPI. Traditionally, random patterns are often adopted for sampling, but this blindly passive strategy requires a high sampling rate, and even so, it is difficult to develop a reconstruction algorithm that can maintain higher accuracy and robustness. In this paper, an active strategy is proposed to perform sampling with targeted scanning by designed patterns, from which the spatial information can be easily reordered well. Then, deep learning methods are introduced further to achieve 3D reconstruction, and the ability of deep learning to reconstruct desired information under low sampling rates are analyzed. Abundant experiments verify that our method improves the precision of SPI even if the sampling rate is very low, which has the potential to be extended flexibly in similar systems according to practical needs.
