This repository contains Python source code for training a deep convolutional neural network to denoise experimental low-counting statistics X-ray diffraction data. It provides the neural-network definitions as well as a training pipeline. The expected format of low- and high-count data is TIF. Furthermore, the data is expected to be located in separate folders named "LC" and "HC" for both training and validation data sets. A helper function for converting the provided (training, validation, and test) HDF5 files at Zenodo (https://doi.org/10.5281/zenodo.8237173) to individual TIF files is given in helper_functions.py, named convert_zenodo_hdf5_to_tif().
Required packages (Python 3):
- numpy
- pandas
- pillow
- scipy
- h5py
- tensorflow 2.4.1 (optional but recommended: tensorflow-gpu)
Original work and data:
- Oppliger, J., Denner, M.M., Küspert, J. et al., Nat Mach Intell 6, 180–186 (2024). https://doi.org/10.1038/s42256-024-00790-1.
- J. Oppliger et al., “X-ray diffraction dataset for experimental noise filtering.” Zenodo, Jul. 24, 2022. https://doi.org/10.5281/zenodo.10816877.