Python script used to generate patches and create CSBDeep model applied to super resolution microscopy.
- Create patches where common spots have been found between image stack pairs.
- Build CSBDeep model
- Test CSBDeep model on images
To work with DatasetMicroscopy :
- Save your image files (.tif) in a data directory
- target for great quality images
- source for poor quality images
Example of directory
- data/target/{file1.ome.tif, file2.ome.tif, file3.ome.tif, file4.ome.tif}
- data/source/{file1.ome.tif, file2.ome.tif, file3.ome.tif, file4.ome.tif}
The equivalent image stacks must have the same names between target and source directories
- Run generateData.py (python generateData.py)
This script will create a npz file containing pair of image patch needed to build the model
- Run createModel.py (python createModel.py)
The script will call CSBDeep functions and use the npz file to train the CARE model (cf CSBDeep getting started in https://github.com/CSBDeep/CSBDeep).
- Run constructImage.py (python constructImage.py)
This function allow to reconstruct an entire stack of images from the CSBDeep model previously trained
This tool uses modified code of picasso (https://github.com/jungmannlab/picasso) for spot localization and modified patches creation function from CSBDeep (https://github.com/CSBDeep/CSBDeep).
Tools developed by Nicolas Riss under the supervison of Julien Godet.