SVRCL-SR: a high spatial resolution imaging method for large-size plate-shaped components

Abstract

Sparse-view rotational-scanning computed laminography (SVRCL) is an essential technique for the rapid inspection of large-size plate-shaped components. However, its reconstructed results inevitably suffer from severe noise and streak artifacts, which degrade detection reliability. Recently, deep learning-based techniques such as projection domain constraints and image domain post-processing have shown promising application prospects. But two major bottlenecks remain. First, convolutional neural networks struggle to capture the long-range distribution characteristics of streak artifacts. Second, self-attention mechanisms introduce additional computational overhead. To solve the above problems, we devise a dual-domain joint image super-resolution (SR) framework (SVRCL-SR). First, shallow structural features are extracted through convolutional layers. Then, we introduce a feature denoising module that focuses on the mid- to high-frequency regions and dynamically performs dual-domain joint denoising. Finally, large-kernel attention is realized via frequency-domain convolution and element-wise multiplication, which compensates for missing high-frequency information while reducing computational cost. Comprehensive evaluations on multiple datasets and SR scales indicate that the SVRCL-SR achieves superior performance in artifact suppression and high-frequency detail restoration, along with strong robustness.