Orthogonal translation computed laminography reconstruction based on self-prior information and adaptive weighted total variation

Abstract

Orthogonal translation computed laminography (OTCL) provides an effective non-destructive testing method for plate-like objects. Nevertheless, OTCL images suffer from aliasing artifacts due to the inherent incompleteness of projection data, negatively impacting flaw characterization, dimensional metrology, and failure analysis. To reveal the cause of aliasing artifacts, the three-dimensional frequency domain characteristics of OTCL are analyzed. We further propose a novel reconstruction algorithm to mitigate aliasing artifacts, termed self-prior information guidance and adaptive weight total variation constraint (SPIG-AwTV). The SPIG-AwTV comprises two components: a self-prior information guidance (SPIG) regularization term and an adaptive weighted total variation (AwTV) regularization term. Specifically, SPIG is derived from filtered backprojection reconstruction result via contour extraction and masking. The AwTV regularization term is tailored to the gradient features of OTCL images in different directions. Experimental results demonstrate that the SPIG-AwTV outperforms existing methods in suppressing aliasing artifacts, preserving edges, and achieving higher-quality OTCL images.