Edge Information Diffusion-Based Reconstruction for Cone Beam Computed Laminography

Abstract

Computed laminography (CL) is a prospective nondestructive testing technique for flat object inspection in industrial applications. However, CL image reconstruction is a challenging task because incomplete projection data are acquired from the CL scan. When a conventional computed tomography (CT) reconstruction method is applied to cone beam CL data, the vertical edges (singularities in the z-direction) in the reconstructed image would be blurred. On the contrary, the horizontal edges (singularities within slices) can be quite accurately reconstructed. Based on this key observation, an edge information diffusion method is developed, which fixes the horizontal edges and propagates their values within the slices. An effective CL reconstruction method is then proposed for flat object inspection by combining the edge information diffusion procedure, which plays the role of regularization, with conventional CT image reconstruction algorithms. Experiments on both simulated data and real data are performed to verify the effectiveness of the proposed method. The results show that the proposed method can effectively suppress the inter-slice aliasing and blurring caused by incompleteness of the CL scan data, and that it outperforms other state-of-the-art methods.