Example data and demo of SFSRM for single-frame super resolution of microscopy images.
SFSRM is built with Python and pytorch.
- Python >= 3.7
- PyTorch >= 1.7
- Clone repo
git clone https://github.com/crrayna/SFSRM.git - Install dependent packages
pip install -r requirements.txt
- Download pretrianed models
Download pretrained models at https://drive.google.com/drive/folders/1UnaDwrt1FNSAUT_OlosqvoV4jsxIxIhi?usp=sharing and put them in the pretrained_network folder
- Inference
python test.py -opt options/test/test_example_microtubule.yml
Results are in the results folder
*For your own test data, we recommend using the SRRF plugin in FIJI/ImageJ to generate the edge map. The plugin provides a 32-bit SRRF image. You will need to convert this image to an 8-bit edge map. Prior to the conversion, it may be necessary to adjust the dynamic range to ensure that the background intensity of your edge map matches the level of our sample edge map. Please note that the background intensity can vary for different samples (e.g., MT, mito, ER), so adjustments might be needed accordingly.
The codes are based on ESRGAN and unetgan. Please also follow their licenses. Thanks for their awesome works.
[1] Wang, Xintao, et al. "Esrgan: Enhanced super-resolution generative adversarial networks." Proceedings of the European conference on computer vision (ECCV) workshops. 2018.
[2] Schonfeld, Edgar, Bernt Schiele, and Anna Khoreva. "A u-net based discriminator for generative adversarial networks." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020.
If you have any questions, please email meshyao@ust.hk