Computed laminography (CL) is one of the best methods for nondestructive testing of plate-like objects. If the object and the detector move continually while the scanning is being done, the data acquisition efficiency of CL will be significantly increased. However, the projection images will contain motion artifact as a result. A multi-angle fusion network (MAFusNet) is presented in order to correct the motion artifact of CL projection images considering the properties of CL projection images. The multi-angle fusion module significantly increases the ability of MAFusNet to deblur by using data from nearby projection images, and the feature fusion module lessens information loss brought on by data flow between the encoders. In contrast to conventional deblurring networks, the MAFusNet network employs synthetic datasets for training and performed well on realistic data, proving the network's outstanding generalization. The multi-angle fusion-based network has a significant improvement in the correction effect of CL motion artifact through ablation study and comparison with existing classical deblurring networks, and the synthetic training dataset can also significantly lower the training cost, which can effectively improve the quality and efficiency of CL imaging in industrial nondestructive testing.
Keywords: Computed laminography; deep learning; image deblurring; rotational motion blur.