Super-resolution reconstruction of industrial PET images using a prior-knowledge-based generative adversarial network

Positron tomography technology (PET) can adapt to complex on-site environments, enabling industrial non-destructive testing without disturbance or damage. PET super-resolution reconstruction aims to reduce detection costs and improve accuracy, making it highly valuable for research. In this study, we propose a generative adversarial network (GAN)-based super-resolution model for industrial PET images that incorporates prior knowledge to address issues such as detail loss and artifact distortion in existing algorithms. We design a texture enhancement network to extract detailed features and employ a connection network to fuse texture and super-resolution features, enhancing texture details. Additionally, we introduce texture loss and super-resolution loss to further improve the model's performance. Experimental results demonstrate that the proposed method enhances super-resolution image quality in both visual and objective evaluation metrics and has been validated in practical industrial detection.