dorsal/arxiv
View SchemaLaminoDiff: Artifact-Free Computed Laminography in Non-Destructive Testing via Diffusion Model
| Authors | Tan Liu, Liu Shi, Binghuang Peng, Tong Jia, Xiaoling Xu, Baodong Liu, Qiegen Liu |
|---|---|
| Categories | |
| ArXiv ID | 2601.07254vv1 |
| URL | https://arxiv.org/abs/2601.07254 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
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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"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.",
"arxiv_id": "2601.07254",
"authors": [
"Tan Liu",
"Liu Shi",
"Binghuang Peng",
"Tong Jia",
"Xiaoling Xu",
"Baodong Liu",
"Qiegen Liu"
],
"categories": [
"eess.IV"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "LaminoDiff: Artifact-Free Computed Laminography in Non-Destructive Testing via Diffusion Model",
"url": "https://arxiv.org/abs/2601.07254",
"version": "v1"
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