Sun Jiawei, Wu Jiachen, Koukourakis Nektarios, Cao Liangcai, Kuschmierz Robert, Czarske Juergen
Laboratory of Measurement and Sensor System Technique (MST), TU Dresden, Helmholtzstrasse 18, 01069, Dresden, Germany.
Competence Center for Biomedical Computational Laser Systems (BIOLAS), TU Dresden, Dresden, Germany.
Sci Rep. 2022 May 11;12(1):7732. doi: 10.1038/s41598-022-11803-7.
The generation of tailored complex light fields with multi-core fiber (MCF) lensless microendoscopes is widely used in biomedicine. However, the computer-generated holograms (CGHs) used for such applications are typically generated by iterative algorithms, which demand high computation effort, limiting advanced applications like fiber-optic cell manipulation. The random and discrete distribution of the fiber cores in an MCF induces strong spatial aliasing to the CGHs, hence, an approach that can rapidly generate tailored CGHs for MCFs is highly demanded. We demonstrate a novel deep neural network-CoreNet, providing accurate tailored CGHs generation for MCFs at a near video rate. The CoreNet is trained by unsupervised learning and speeds up the computation time by two magnitudes with high fidelity light field generation compared to the previously reported CGH algorithms for MCFs. Real-time generated tailored CGHs are on-the-fly loaded to the phase-only spatial light modulator (SLM) for near video-rate complex light fields generation through the MCF microendoscope. This paves the avenue for real-time cell rotation and several further applications that require real-time high-fidelity light delivery in biomedicine.
利用多芯光纤（MCF）无透镜微内窥镜生成定制的复杂光场在生物医学中得到了广泛应用。然而，用于此类应用的计算机生成全息图（CGH）通常由迭代算法生成，这需要大量的计算量，限制了诸如光纤细胞操作等先进应用。MCF中光纤芯的随机和离散分布会给CGH带来强烈的空间混叠，因此，迫切需要一种能够快速为MCF生成定制CGH的方法。我们展示了一种新颖的深度神经网络——CoreNet，它能够以接近视频的速率为MCF准确生成定制的CGH。CoreNet通过无监督学习进行训练，与之前报道的用于MCF的CGH算法相比，在生成高保真光场时，将计算时间加快了两个数量级。实时生成的定制CGH被实时加载到纯相位空间光调制器（SLM）上，通过MCF微内窥镜生成接近视频速率的复杂光场。这为生物医学中实时细胞旋转以及其他需要实时高保真光传输的应用铺平了道路。
