Cone-beam computed tomography based on truncated adaptive-weight total variation

It is challenging to get reconstruct high-quality images for a Computed laminography scan. CL images always suffer inter-slices aliasing and blurring since projections from the CL scanning are incomplete. To solve this problem, we propose an iterative reconstruction algorithm constrained by a truncated adaptive-weight total variation (TAwTV). In detail, the image gradient amplitude is first truncated according to a threshold, and then we design a cosine nonlinear function of truncated gradient amplitude to adjust the truncated gradient adap-tively thus the truncated adaptive-weight total variation can overcome over-smoothing when penalizing larger gradient amplitude and isotropic property. Experiments on both simulated 3D printed circuit board, simulated workpiece and Shepp-Logan phantom show that the proposed algorithm has noticeable results in artifact sup-pression and edge-preserving.