Cone Beam Computed Laminography Based on Generalized Charbonnier Penalty

Cone beam computed laminography (CL) has become one of the best methods for nondestructive testing of plate objects because of its particular scanning geometry. However, a challenging task in CL imaging is to deal with inter-slice aliasing caused by incomplete projection data from CL scanning. Using the classical total variation (TV) reconstruction method can reduce aliasing to a certain extent, but it often makes the image too smooth. In order to improve the quality of reconstructed CL image, a CL reconstruction algorithm based on generalized Charbonnier penalty is proposed in this paper. In detail, the TV regular term is replaced by a non-convex generalized Charbonnier penalty, and different weights are assigned to different directions. The original problem is divided into two subproblems to solve separately. The first subproblem uses Chambolle and Pock (CP) algorithm to solve the fidelity term, and the second subproblem uses additive half-quadratic minimization algorithm to denoise the image. Experiments were conducted using simulated PCB phantom, simulated workpiece phantom, and real data to verify the effectiveness of the proposed algorithm. The experimental results through visual assessment and quantitative assessment (including RMSE, MSSIM, PCC, and UQI indicators) demonstrate that the proposed method is effective in preserving image features and suppressing inter-slice aliasing in reconstructed CL images. The MATLAB code for the proposed algorithm is available at https://github.com/LIUyi827728/CL-Generalize-Charbonnier.