Yao Bowen, Li Wen, Pan Wenhui, Yang Zhigang, Chen Danni, Li Jia, Qu Junle
Opt Express. 2020 May 11;28(10):15432-15446. doi: 10.1364/OE.392358.
An accurate and fast reconstruction algorithm is crucial for the improvement of temporal resolution in high-density super-resolution microscopy, particularly in view of the challenges associated with live-cell imaging. In this work, we design a deep network based on a convolutional neural network to take advantage of its enhanced ability in high-density molecule localization, and introduce a residual layer into the network to reduce noise. The proposed scheme also incorporates robustness against variations of both the full width at half maximum (FWHM) and the pixel size. We validate our algorithm on both simulated and experimental data by achieving performance improvement in terms of loss value and image quality, and demonstrate live-cell imaging with temporal resolution of 0.5 seconds by recovering mitochondria dynamics.
一种准确且快速的重建算法对于提高高密度超分辨率显微镜的时间分辨率至关重要，尤其是考虑到与活细胞成像相关的挑战。在这项工作中，我们基于卷积神经网络设计了一个深度网络，以利用其在高密度分子定位方面增强的能力，并在网络中引入残差层以减少噪声。所提出的方案还具有针对半高全宽（FWHM）和像素大小变化的鲁棒性。我们通过在损失值和图像质量方面实现性能提升，在模拟数据和实验数据上验证了我们的算法，并通过恢复线粒体动力学展示了时间分辨率为0.5秒的活细胞成像。
