Super-resolution reconstruction of synchrotron radiation nano-CT images and its applications

BackgroundSynchrotron radiation nano-computed tomography (nano-CT) is widely applied in materials science, energy research, and geology due to its capability to provide high-resolution three-dimensional structural information at the nanoscale. However, environmental interference, equipment limitations, and light source fluctuations often degrade image quality, leading to reduced signal-to-noise ratio, blurring, and loss of critical details, which severely impact quantitative analysis and segmentation accuracy for microstructures such as cracks and pores. While existing super-resolution (SR) techniques for natural images have significant advantages, their adaptation to nano-CT remains challenged by dataset scarcity and stringent reconstruction accuracy requirements.PurposeThis study aims to develop a dedicated super-resolution reconstruction network for synchrotron radiation nano-CT images to enhance the clarity and detail restoration of low-quality CT images and investigate its efficacy in optimizing downstream tasks such as crack segmentation.MethodsFirstly, a Transformer-based U-shaped symmetric network for SR (UTSR) was proposed, integrating a Double Convolutional Feedforward Network (DCFFN) and a Grouped Multi-scale Window Self-Attention (GMSA) mechanism to achieve efficient reconstruction through multi-level feature extraction and hierarchical processing. Then, a transfer learning strategy was adopted, leveraging natural image datasets for pre-training and fine-tuning the model with nano-CT data from the Shanghai Synchrotron Radiation Facility (SSRF) to address dataset scarcity. Finally, a Sobel edge loss function was introduced to prioritize texture preservation, and a two-stage training framework (general image pre-training and nano-CT fine-tuning) was implemented to optimize model performances in terms of Natural Image Quality Evaluator (NIQE), Learned Perceptual Image Patch Similarity (LPIPS), Deep Image Structure and Texture Similarity (DISTS), Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM).ResultsComparison results show that UTSR demonstrates superior performance on both simulated and nano-CT datasets: it achieves lower NIQE (9.38) and LPIPS (0.312) scores compared to SwinIR and Real-ESRGAN, alongside reduced computational complexity (45.72G FLOPs). When applied to crack segmentation, UTSR-processed images attain an accuracy of 99.3%, with a 24.5% improvement in recall and a 19.1% increase in F1 Score over original images. Ablation experiments confirm the effectiveness of the DCFFN module, where its combination with GMSA further reduces NIQE to 10.99.ConclusionsThe UTSR network effectively addresses the challenges of low-quality synchrotron radiation nano-CT images and scarce datasets through its innovative architecture and transfer learning strategy, providing a robust preprocessing solution for microstructural analysis, hence expands the application of deep learning-based super-resolution in scientific imaging, offering substantial academic and engineering value.