基于光栅的X射线干涉成像中像域莫尔伪影自动消除方法
Automatic image-domain Moire artifact reduction method in grating-based x-ray interferometry imaging
摘要：
本研究的目的是证明通过卷积神经网络（CNN）技术消除光栅X射线干涉成像系统中系统误差引起的图像莫尔伪影的可行性。我们新提出的基于CNN的方法不是通过某些优化的信号检索算法来最小化所获取的相位步进数据之间的这些不一致，而是通过学习的图像后处理过程来减少图像域中的莫尔伪影。为了简化训练数据的准备，我们建议将它们与数值自然图像和实验获得的莫尔伪影图像合成。此外，还开发了一种快速信号处理方法，从有限数量的实验相位步进数据中生成所需的大量高质量的仅含莫尔伪影的图像。实验结果表明，CNN方法能够在保持信号精度和图像分辨率的前提下有效去除莫尔伪影。
英文摘要：
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.
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