  We propose a deep learning algorithm for the inpainting of Bragg Coherent
Diffraction Imaging (BCDI) patterns affected by detector gaps. These regions of
missing intensity can compromise the accuracy of reconstruction algorithms,
inducing artifacts in the final result. It is thus desirable to restore the
intensity in these regions in order to ensure more reliable reconstructions.
The key aspect of our method lies in the choice of training the neural network
with cropped sections of both experimental diffraction data and simulated data
and subsequently patching the predictions generated by the model along the gap,
thus completing the full diffraction peak. This provides us with more
experimental training data and allows for a faster model training due to the
limited size, while the neural network can be applied to arbitrarily larger
BCDI datasets. Moreover, our method not only broadens the scope of application
but also ensures the preservation of data integrity and reliability in the face
of challenging experimental conditions.
