A novel detail-enhanced wavelet domain feature compensation network for sparse-view X-ray computed laminography.

Abstract

Background: X-ray Computed Laminography (CL) is a popular industrial tool for non-destructive visualization of flat objects. However, high-quality CL imaging requires a large number of projections, resulting in a long imaging time. Reducing the number of projections allows acceleration of the imaging process, but decreases the quality of reconstructed images.

Objective: Our objective is to build a deep learning network for sparse-view CL reconstruction.

Methods: Considering complementarities of feature extraction in different domains, we design an encoder-decoder network that enables to compensate the missing information during spatial domain feature extraction in wavelet domain. Also, a detail-enhanced module is developed to highlight details. Additionally, Swin Transformer and convolution operators are combined to better capture features.

Results: A total of 3200 pairs of 16-view and 1024-view CL images (2880 pairs for training, 160 pairs for validation, and 160 pairs for testing) of solder joints have been employed to investigate the performance of the proposed network. It is observed that the proposed network obtains the highest image quality with PSNR and SSIM of 37.875 ± 0.908 dB, 0.992 ± 0.002, respectively. Also, it achieves competitive results on the AAPM dataset.

Conclusions: This study demonstrates the effectiveness and generalization of the proposed network for sparse-view CL reconstruction.