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

TL;DR

LaminoDiff is a novel diffusion model-based framework that effectively removes artifacts in computed laminography imaging, enabling high-fidelity visualization of internal structures in large objects for non-destructive testing.

Contribution

It introduces a diffusion model integrated with a dual-modal CT-CL fusion prior to bridge the domain gap and improve artifact removal in laminography imaging.

Findings

Achieves high-fidelity reconstruction on simulated and real PCB datasets.

Effectively suppresses artifacts while preserving geometric details.

Facilitates reliable automated defect recognition.

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.