RCL-Mamba：面向旋转稀疏视角扫描计算机层析成像中测量导向图像恢复的双域状态空间模型
RCL-Mamba: A Dual-domain State Space Model for Measurement-oriented Image Restoration in Rotational Sparse-View Scanning Computed Laminography
AI总结：
提出RCL-Mamba，一种面向测量的双域状态空间模型，通过级联处理校正投影域旋转模糊并抑制图像域稀疏伪影，采用Mamba-CNN双分支模块平衡大尺度校正与细节恢复，将扫描视角从512减至64，检测效率提升约8倍，在PCB扫描中优于现有方法。
摘要：
旋转扫描计算机层析成像（RCL）广泛应用于大型平面部件的无损检测（NDT）。然而，为了促进快速检测，常采用连续稀疏视角扫描，其中曝光期间的角积分效应在投影域引入旋转模糊。此外，稀疏采样固有的数据不完整性在重建图像域表现为稀疏伪影。为解决这些跨域退化问题，本文提出RCL-Mamba，一种面向测量的双域状态空间模型（SSM）图像恢复网络。该框架采用级联联合处理策略：首先校正投影域中的旋转模糊，随后抑制图像域中的稀疏伪影。此外，我们设计了一个Mamba-CNN双分支模块，以自适应地平衡大尺度模糊校正与局部细节恢复。在模拟数据集和真实印刷电路板（PCB）扫描上的评估表明，RCL-Mamba在模糊去除、伪影抑制和结构保持方面优于现有基线。基于线轮廓的结构测量进一步验证了所提方法更好地保留了通孔/焊盘边界和细长走线轮廓。关键在于，通过将所需扫描视角从512减少到64，我们的方法在不牺牲重建质量的情况下将检测效率提高了约8倍，为高吞吐量RCL检测提供了一种稳健的面向测量的恢复解决方案，并提高了结构测量保真度。
英文摘要：
Rotational Scanning Computed Laminography (RCL) is widely utilized for the Non-Destructive Testing (NDT) of large planar components. However, to facilitate rapid inspection, continuous sparse-view scanning is often employed, where the angular integration effect during exposure induces rotational blur in the projection domain. Furthermore, the data incompleteness inherent in sparse sampling manifests as sparse artifacts in the reconstructed image domain. To address these cross-domain degradations, this paper proposes RCL-Mamba, a measurement-oriented dual-domain State Space Model (SSM)-based image restoration network. The framework adopts a cascaded joint processing strategy: it first corrects the rotational blur in the projection domain and subsequently suppresses the sparse artifacts in the image domain. Additionally, we design a Mamba-CNN dual-branch module to adaptively balance large-scale blur correction with local detail recovery. Evaluations on both simulated datasets and real-world Printed Circuit Board (PCB) scans demonstrate that RCL-Mamba outperforms existing baselines in blur removal, artifact suppression, and structural preservation. Line-profile-based structural measurement further verifies that the proposed method better preserves via/pad boundaries and slender trace profiles. Crucially, by reducing the required scanning views from 512 to 64, our method enhances inspection efficiency by approximately 8-fold without compromising reconstruction quality, offering a robust measurement-oriented restoration solution for high-throughput RCL inspection with improved structural measurement fidelity.
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