SFBMT A fast super-resolution imaging technique based on single-molecule localization and deep learning

This software provides a fluorescence super resolution imaging tool based on deep learning and single molecule localization images, which can be applied to a variety of hardware systems, including TIRF(total internal reflection fluorescence microscope), WF(wide-field microscope), SIM(structured illumination microscope), etc., to improve the imaging quality of the original imaging system. It has the following characteristics:

1) This method takes advantage of the high resolution and sparsity characteristics of single-molecule localization modality to regularize the deep learning super-resolution results, to achieve higher resolution and fewer artifacts.

2) This method breaks the limitation of low time resolution of single molecule localization microscopy and can be applied in living cell imaging.

3) This method can learn the physical degradation model specific to the microscope, and after calibration, the super-resolution reconstruction has good generalization and robustness for various cell structures.