Aug 2026· Measurement science and technology· Vol 37· 0 citations· 42 references
Physics
TL;DR
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.
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.
Deep learning–based multi-contrast Magnetic Resonance (MCMR) super-resolution (SR) has achieved notable success in accelerating image acquisition and improving image quality. However, significant challenges remain when dealing with the volumetric data: 1) Most existing MCMR SR methods primarily rely on single-slice information and fail to exploit high-dimensional volumetric contextual information; 2) Due to the sparsity of the original low-resolution volumetric data, conventional small kernel convolutions struggle to capture long-range contextual information. Although transformer-based approaches can model long-range dependencies, they suffer from high computational and memory demands when applied to high-dimensional volumetric data. To address these challenges, we propose a multi-view large-kernel attention network for MCMR volumetric SR. The method contains three stages: a cross-modality synthesis stage, an inter-slice deformable compensation stage, and a multi-view large-kernel attention fusion stage. Specifically, a multi-view fusion strategy is proposed to exploit the rich spatial contextual information inherent in high-dimensional volumetric data. A large-kernel convolution attention block is proposed to efficiently capture long-range dependencies from the sparsely sampled coronal and sagittal planes. By jointly integrating the high-order multi-view and multi-contrast information, our method successfully reconstructs high-quality MCMR volumetric data. Experimental results across different datasets, along with the downstream segmentation tasks, attest to the effectiveness of the proposed method.
Pengcheng Lei, Juncheng Li, Faming Fang et al.· IEEE Transactions on Computa...· 0 citations
Residual Flow Matching for Image Super-Resolution (RFMSR) is proposed, a vision-only framework that centers the source distribution at the LQ latent, reducing transport distance and preserving structural priors throughout the flow trajectory.
Shuwei Huang, Tianyao Luo, Jicheng Liu et al.· 1 citation
The results indicate that the collaboration between global semantics and local details within a unified weighting domain can effectively improve the separability and deploy ability of high-resolution segmentation.
Yuyang Wang, Jiamei Hu, Xinwei Wang et al.· International Conference on...· 0 citations
An enhancement pipeline that operates entirely within classical signal processing is proposed, providing a transparent alternative to black-box machine learning methods while remaining practical on standard personal computers.
S. Bhattacharya· Asian journal of applied sci...· 0 citations
Experimental results demonstrate that LightVM-SparseUNet achieves segmentation competitive with state-of-the-art large-scale models across two authoritative public datasets.
Haojie Fan, Kang Xu, Xiaoyu Hou et al.· Biomedical engineering and p...· 0 citations
A robust attention-guided multi-dimensional feature fusion network (LFAMF) for LF angular reconstruction that significantly outperforms state-of-the-art methods, particularly in maintaining structural integrity at occlusion boundaries and highly textured areas while ensuring superior angular consistency.
Xiyao Hua, B. Su, Daili Yang· PLoS ONE· 0 citations
