DFCAN/DFGAN software is the tensorflow/keras implementation for image transformation from low-resolution (LR) image to super-resolved one, including single wide-field (WF) image super-resolution prediction and SIM reconstruction. This repository is developed based on the 2021 Nature Methods paper Evaluation and development of deep neural networks for image super-resolution in optical microscopy.

Author: Chang Qiao^1,#, Di Li^2,#, Yuting Guo^2,#, Chong Liu^2,3,#, Tao Jiang^2,3, Qionghai Dai^1,+, Dong Li^2,3,4,+
1Department of Automation, Tsinghua University, Beijing, China.
²National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.
³College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China.
⁴Bioland Laboratory, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China.
^#Equal contribution.
⁺Correspondence to: qhdai@tsinghua.edu.cn and lidong@ibp.ac.cn

• Environment
• File structure
• BioSR dataset
• Test pre-trained models
• Train a new model
• License
• Citation

• Ubuntu 16.04
• CUDA 9.0.16
• Python 3.6.10
• Tensorflow 1.10.0
• Keras 2.2.4
• GPU: GeForce RTX 2080Ti

• ./dataset is the default path for training data and testing data
  • ./dataset/train The augmented training image patch pairs will be saved here by default
  • ./dataset/test includes some demo images of F-actin and microtubules to test DFCAN/DFGAN models
• ./src includes the source codes of DFCAN and DFGAN
  • ./src/models includes declaration of DFCAN and DFGAN models
  • ./src/utils is the tool package of DFCAN/DFGAN software
• ./trained_models place pre-trained DFGAN/DFCAN models here for testing, and newly trained models will be saved here by default
• ./data_agmt_matlab includes matlab codes used for data augmentation (matlab version: MATLAB 2017b)

BioSR is a biological image dataset for super-resolution microscopy, currently including more than 2200 pairs of low-and-high resolution images covering four biology structures (CCPs, ER, MTs, F-actin), nine signal levels (15-600 average photon count), and two upscaling-factors (linear SIM and non-linear SIM). BioSR is now freely available, aiming to provide a high-quality dataset for the community of single bio-image super-resolution algorithm and advanced SIM reconstruction algorithm developers.

• Download pre-trained models of DFCAN/DFGAN and place them in ./trained_models/
• Download test data and place them in ./dataset/test. Also, you can download BioSR for more testing data
• Open your terminal and cd to ./src
• Run bash demo_predict.sh in your terminal. Note that before running the bash file, you should check if the data paths and other arguments in demo_predict.sh are set correctly
• The output SR images will be saved in --data_dir
• Typical results:
  

• Data for training: You can train a new DFCAN/DFGAN model using BioSR or your own datasets. Note that you'd better divide the dataset of each specimen into training part and validation/testing part before training, so that you can test your model with the preserved validation/testing data
• Data augumentation: run ./data_agmt_matlab/DataAugmentation_ForTrain.m with MATLAB to creat image patch pairs of BioSR datasets. Before running, you should check image paths and some parameters following the instructions in ./data_agmt_matlab/DataAugumentation_ForTrain.m. After running, the augumented data is saved in ./dataset/train by default
• Run bash demo_train.sh in your terminal to train a new DFCAN model. Similar to testing, before running the bash file, you should check if the data paths and the arguments are set correctly
• You can run tensorboard --logdir [save_weights_dir]/[save_weights_name]/graph to monitor the training process via tensorboard. If the validation loss isn't likely to decay any more, you can use early stop strategy to end the training
• Model weights will be saved in ./trained_models/ by default

This repository is released under the MIT License (refer to the LICENSE file for details).

If you find the code or BioSR dataset helpful in your resarch, please cite the following paper:

@article{nmeth2021DFCAN,
  title={Evaluation and development of deep neural networks for image super-resolution in optical microscopy},       
  author={Chang Qiao, Di li, Yuting Guo, Chong Liu, Tao Jiang, Qionghai Dai and Dong Li},
  journal={Nature Methods},
  pages={},
  year={2021},
  publisher={Nature Publishing Group}
}