LaminoDiff: Artifact-Free Computed Laminography in Non-Destructive Testing via Diffusion Model
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914247737344000 |
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| author | Liu, Tan Shi, Liu Peng, Binghuang Jia, Tong Xu, Xiaoling Liu, Baodong Liu, Qiegen |
| author_facet | Liu, Tan Shi, Liu Peng, Binghuang Jia, Tong Xu, Xiaoling Liu, Baodong Liu, Qiegen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_07254 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LaminoDiff: Artifact-Free Computed Laminography in Non-Destructive Testing via Diffusion Model Liu, Tan Shi, Liu Peng, Binghuang Jia, Tong Xu, Xiaoling Liu, Baodong Liu, Qiegen Image and Video Processing 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. |
| title | LaminoDiff: Artifact-Free Computed Laminography in Non-Destructive Testing via Diffusion Model |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2601.07254 |