Medical Image Super-Resolution Reconstruction Based on Multi-Level Adaptive CNN and Hybrid Transformer

With the rapid advancements in medical imaging technology, super-resolution reconstruction techniques are crucial for enhancing disease diagnosis and treatment precision. However, the inherent resolution limitations of imaging devices and the clinical need for low-dose imaging often result in insufficient resolution. This deficiency leads to a loss of detailed information, adversely affecting diagnostic accuracy. Improving image resolution without increasing radiation dose has therefore become a critical issue in medical imaging. Addressing this challenge, this study proposes an innovative super-resolution reconstruction method for medical images, the Multi-Scale adaptive Convolutional Neural Network (CNN) and Hybrid Transformer model (MSCT). The core of this method is a multi-scale adaptive convolutional module with residual connections that effectively extracts local features of multi-scale images, overcoming traditional CNNs’ limitations in handling complex textures. Additionally, a hybrid Transformer module enhances the perception of global features and captures texture details by leveraging long-range dependencies and global information modeling. By combining the adaptive convolutional module with the Transformer module, this method effectively fuses local and global information, significantly improving image reconstruction accuracy. Experimental results on CT and MRI datasets show that the proposed method achieved average values of 21.96 dB in Peak Signal-to-Noise Ratio (PSNR) and 0.8053 in Structural Similarity Index (SSIM). These results surpass most mainstream methods, demonstrating superior performance in recovering image details and texture information, providing strong technical support for the diagnosis and analysis of medical images.