  Super-resolution fluorescence microscopy, with a resolution beyond the
diffraction limit of light, has become an indispensable tool to directly
visualize biological structures in living cells at a nanometer-scale
resolution. Despite advances in high-density super-resolution fluorescent
techniques, existing methods still have bottlenecks, including extremely long
execution time, artificial thinning and thickening of structures, and lack of
ability to capture latent structures. Here we propose a novel deep learning
guided Bayesian inference approach, DLBI, for the time-series analysis of
high-density fluorescent images. Our method combines the strength of deep
learning and statistical inference, where deep learning captures the underlying
distribution of the fluorophores that are consistent with the observed
time-series fluorescent images by exploring local features and correlation
along time-axis, and statistical inference further refines the ultrastructure
extracted by deep learning and endues physical meaning to the final image.
Comprehensive experimental results on both real and simulated datasets
demonstrate that our method provides more accurate and realistic local patch
and large-field reconstruction than the state-of-the-art method, the 3B
analysis, while our method is more than two orders of magnitude faster. The
main program is available at https://github.com/lykaust15/DLBI
