This repository contains the codes and the instructions for the implementation using Tensorflow v.2.2.0 of a deep learning model for the 3D image inpainting. It has been specifically designed for 3D Bragg X-ray diffraction patterns affected by detectors gaps.
The structure of the repository is the following:
- data: BCDI files
- models: saved weights of the models for the inpainting of gaps with size of 3, 6, 9, 12 pixels
- notebooks: Jupyter notebooks for the handling of the model, the plotting of figures and assessment of model performance
- src: Jupyter notebooks for the creation/training of the models and for the application to entire BCDI patterns.
I developped the codes during my PhD at the University Grenoble - Alpes and at the ID01 beamline of the European Synchrotron Radiation Facility (ESRF-EBS). The PhD is also part of the ENGAGE programme, thus partially funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement number 101034267.
