Tang Ming, He Hao, Yu Longkun
School of Advanced Manufacturing, Nanchang University, Nanchang, Jiangxi 330031, China.
Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China.
Biomed Opt Express. 2022 Aug 1;13(8):4455-4467. doi: 10.1364/BOE.463678.
Digital holographic microscopy (DHM) has the potential to reconstruct the 3D shape of volumetric samples from a single-shot hologram in a label-free and noninvasive manner. However, the holographic reconstruction is significantly compromised by the out-of-focus image resulting from the crosstalk between refocused planes, leading to the low fidelity of the results. In this paper, we propose a crosstalk suppression algorithm-assisted 3D imaging method combined with a home built DHM system to achieve accurate 3D imaging of ocean algae using only a single hologram. As a key step in the algorithm, a hybrid edge detection strategy using gradient-based and deep learning-based methods is proposed to offer accurate boundary information for the downstream processing. With this information, the crosstalk of each refocused plane can be estimated with adjacent refocused planes. Empowered by this method, we demonstrated successful 3D imaging of six kinds of ocean algae that agree well with the ground truth; we further demonstrated that this method could achieve real-time 3D imaging of the quick swimming ocean algae in the water environment. To our knowledge, this is the first time single-shot DHM is reported in 3D imaging of ocean algae, paving the way for on-site monitoring of the ocean algae.
数字全息显微镜(DHM)有潜力以无标记、非侵入的方式从单次全息图重建体积样本的三维形状。然而,由于重聚焦平面之间的串扰导致的离焦图像,全息重建受到显著影响,从而导致结果的低保真度。在本文中,我们提出了一种串扰抑制算法辅助的三维成像方法,并结合自行搭建的DHM系统,仅使用单个全息图实现对海洋藻类的精确三维成像。作为该算法的关键步骤,提出了一种结合基于梯度和基于深度学习方法的混合边缘检测策略,为下游处理提供准确的边界信息。利用这些信息,可以与相邻重聚焦平面一起估计每个重聚焦平面的串扰。在该方法的支持下,我们成功地对六种海洋藻类进行了三维成像,结果与真实情况高度吻合;我们还进一步证明了该方法能够在水环境中对快速游动的海洋藻类实现实时三维成像。据我们所知,这是首次在海洋藻类的三维成像中报道单次DHM,为海洋藻类的现场监测铺平了道路。