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arXiv · 2601.07254

LaminoDiff: Artifact-Free Computed Laminography in Non-Destructive Testing via Diffusion Model

Tan Liu·Liu Shi·Binghuang Peng·Tong Jia·Xiaoling Xu·Baodong Liu·Qiegen Liu
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Abstract

Computed Laminography (CL) is a key non-destructive testing technology for the visualization of internal structures in large planar objects. The inherent scanning geometry of CL inevitably results in inter-layer aliasing artifacts, limiting its practical application, particularly in electronic component inspection. While deep learning (DL) provides a powerful paradigm for artifact removal, its effectiveness is often limited by the domain gap between synthetic data and real-world data. In this work, we present LaminoDiff, a framework to integrate a diffusion model with a high-fidelity prior representation to bridge the domain gap in CL imaging. This prior, generated via a dual-modal CT-CL fusion strategy, is integrated into the proposed network as a conditional constraint. This integration ensures high-precision preservation of circuit structures and geometric fidelity while suppressing artifacts. Extensive experiments on both simulated and real PCB datasets demonstrate that LaminoDiff achieves high-fidelity reconstruction with competitive performance in artifact suppression and detail recovery. More importantly, the results facilitate reliable automated defect recognition.

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Tan Liu, Liu Shi, Binghuang Peng, Tong Jia, Xiaoling Xu, Baodong Liu, Qiegen Liu. 2026-01-12. LaminoDiff: Artifact-Free Computed Laminography in Non-Destructive Testing via Diffusion Model. https://arxiv.org/abs/2601.07254

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