Super-Resolution Spatiotemporal information integration，SRST
We developed a deep learning-driven approach, termed super-resolution spatiotemporal information integration (SRST), for the precise three-dimensional localization of ultra-high-density molecules.
Link: GitHub – XuLab-BIT/SRST
INSPR (in situ PSF retrieval)
INSPR toolbox is developed for both biplane and astigmatism-based setups. It constructs an in situ 3D point spread function (PSF) directly from the obtained single molecule dataset and features an easy-to-use user interface including all steps of 3D single molecule localization from INSPR model generation, pupil-based 3D localization (supporting both GPU with cubic spline implementation and CPU versions), drift correction, volume alignment, to super-resolution image reconstruction. It also contains a small single molecule dataset for users to run as an example.
Link: GitHub – HuanglabPurdue/INSPR: in situ PSF retrieval for whole cells and tissues
Live-SIMBA: an ImageJ plug-in for the universal and accelerated single molecule-guided Bayesian localization super resolution microscopy (SIMBA) method
we propose a universal and accelerated SIMBA ImageJ plug-in, Live-SIMBA, for time-series analysis in living cells. Live-SIMBA circumvents the requirement of dual-channel dataset using intensity-based sampling algorithm and improves the computing speed using multi-core parallel computing technique.
Link: Live_SIMBA Software
DLBI: Deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy
Here, we propose a novel deep learning guided Bayesian inference (DLBI) approach, 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
Link: GitHub – liyu95/DLBI: DLBI: Deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy