# Automatic image-domain Moire artifact reduction method in grating-based x-ray interferometry imaging

## TL;DR

This paper presents a CNN-based post-processing method to effectively remove Moire artifacts in grating-based x-ray interferometry images, improving image quality without compromising resolution.

## Contribution

The study introduces a novel CNN approach trained on synthesized and experimental data to reduce Moire artifacts in the image domain of x-ray interferometry.

## Key findings

- Effective Moire artifact removal demonstrated
- Maintains signal accuracy and image resolution
- Fast synthesis of training data from experimental images

## Abstract

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.