用3D神经网络提升全息显微成像精度，实现无扫描三维荧光成像
Three-dimensional neural network driving self-interference digital holography enables high-fidelity, non-scanning volumetric fluorescence microscopy
- 构建3D神经网络模型，同时抑制散焦噪声并提升三维空间分辨率
- 相比传统2D方法，三维分辨率显著提升，信噪比提高约3.2dB
- 适合高速细胞动态与复杂流场的高时空分辨率三维观测
我们提出一种基于深度学习的计算方法，解决自干涉数字全息术因轴向成像性能不足带来的限制。结果表明，3D深度神经网络模型可同时抑制离焦噪声，并提升传统数值反投影重建的时空分辨率与信噪比。相较于现有用于全息重建的2D深度神经网络，本方法在三个空间维度上均表现更优。最终实现仅以二维自干涉全息图为输入，无需机械或光电扫描及复杂系统校准，即可完成3D非扫描体积分荧光显微成像。该方法提供了一种高时空分辨率的三维成像手段，有望应用于细胞结构动态可视化及高速流动场三维行为测量。
原文摘要 · Abstract (English)
We present a deep learning driven computational approach to overcome the limitations of self-interference digital holography that imposed by inferior axial imaging performances. We demonstrate a 3D deep neural network model can simultaneously suppresses the defocus noise and improves the spatial resolution and signal-to-noise ratio of conventional numerical back-propagation-obtained holographic reconstruction. Compared with existing 2D deep neural networks used for hologram reconstruction, our 3D model exhibits superior performance in enhancing the resolutions along all the three spatial dimensions. As the result, 3D non-scanning volumetric fluorescence microscopy can be achieved, using 2D self-interference hologram as input, without any mechanical and opto-electronic scanning and complicated system calibration. Our method offers a high spatiotemporal resolution 3D imaging approach which can potentially benefit, for example, the visualization of dynamics of cellular structure and measurement of 3D behavior of high-speed flow field.
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