  The aim of this study is to demonstrate the feasibility of removing the image
Moire artifacts caused by system inaccuracies in grating-based x-ray
interferometry imaging system via convolutional neural network (CNN) technique.
Instead of minimizing these inconsistencies between the acquired phase stepping
data via certain optimized signal retrieval algorithms, our newly proposed
CNN-based method reduces the Moire artifacts in the image-domain via a learned
image post-processing procedure. To ease the training data preparations, we
propose to synthesize them with numerical natural images and experimentally
obtained Moire artifact-only-images. Moreover, a fast signal processing method
has also been developed to generate the needed large number of high quality
Moire artifact-only images from finite number of acquired experimental phase
stepping data. Experimental results show that the CNN method is able to remove
Moire artifacts effectively, while maintaining the signal accuracy and image
resolution.
