FID-STORM is a real-time processing method for single molecule localization microscopy with deep learning, which is based on improved residual convolutional network. This method can achieve real-time processing of raw images up to 256×256 pixels @ Nvidia RTX 2080 Ti graphic card with a speed of 7.31 ms/frame, shorter than the typical exposure time of 10~30 ms. Moreover, compared with a popular interpolated image-based method called Deep-STORM, FID-STORM enables a speed gain of ~25 times, without loss of reconstruction accuracy.
The following picture shows the schematic comparison of proposed FID-STORM with the conventional Interpolation-based SMLM model.
For more details, please refer to User‘s Guide of FID-STORM.pdf and our paper Deep learning using residual deconvolutional network enables real-time high-density single-molecule localization microscopy.
The repository consists of 3 folders, that are data, ImageJ plugin, and Source code.
| Folders | Function |
|---|---|
| data | Consists of relative model input and output data. |
| ImageJ plugin | A .jar file, which can be loaded by ImageJ as a plugin. |
| Source code | 1) The source code of FID-STORM with c++ and java in folder cplusplus and Java using for inferring; 2) The source code of FID-STORM with python in folder python using for training a model |
Reconstruct 10000 raw images with 256 × 256 pixels (that is 27.4 μm × 27.4 μm) in real time.
There are two licenses for different part of the ANNA-PALM code: a MIT license is applied to files inside the AnetLib folder. A Non-commercial License Agreement is applied to all other files.
Declaration This program is free software: you can redistribute it and/or modify it under the terms of the GNU LESSER GENERAL PUBLIC LICENSE as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU LESSER GENERAL PUBLIC LICENSE for more details.
You should have received a copy of the GNU LESSER GENERAL PUBLIC LICENSE along with this program. If not, see https://www.gnu.org/licenses/.
For more questions, please contact Prof. Zhengxia Wang at "[email protected]" or author Zhiwei Zhou at "[email protected]".