A novel spatial and temporal super-resolution (SR) framework based on a recurrent neural network (RNN) is demonstrated. In this work, we learn the complex yet useful features from the temporal data by taking advantage of structural characteristics of RNN and a skip connection. The usage of supervision mechanism is not only making full use of the intermediate output of each recurrent layer to recover the final output, but also alleviating vanishing/exploding gradients during the back-propagation. The proposed scheme achieves excellent reconstruction results, improving both the spatial and temporal resolution of fluorescence images including the simulated and real tubulin datasets. Besides, robustness against various critical metrics, such as the full-width at half-maximum (FWHM) and molecular density, can also be incorporated. In the validation, the performance can be increased by more than 20% for intensity profile, and 8% for FWHM, and the running time can be saved at least 40% compared with the classic Deep-STORM method, a high-performance net which is popularly used for comparison.

中文翻译：

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循环神经网络用于荧光显微镜的时空超分辨率

演示了一种基于递归神经网络（RNN）的新型时空超分辨率（SR）框架。在这项工作中，我们通过利用RNN的结构特征和跳过连接来从时间数据中学习复杂而有用的功能。监督机制的使用不仅充分利用了每个循环层的中间输出来恢复最终输出，而且还减轻了反向传播过程中的消失/爆炸梯度。所提出的方案实现了出色的重建结果，改善了包括模拟和真实微管蛋白数据集的荧光图像的时空分辨率。此外，还可以结合针对各种关键指标（如半峰全宽（FWHM）和分子密度）的鲁棒性。在验证中，