Implicit Function Super-Resolution Reconstruction Based on Group Propagation Vision Transformer

Reconstruction-based single-image super-resolution methods, while demonstrating excellent performance, often face challenges such as training instability, artifact generation, information loss, and insufficient control over global information. To address these challenges, we propose an implicit function super-resolution reconstruction algorithm based on Group Propagation Vision Transformer (GP-ViT). This method employs GP-ViT as an encoder to efficiently capture global contextual information through a group propagation mechanism, while reducing computational complexity and memory consumption, and significantly enhancing local feature extraction capabilities. In the decoding phase, the algorithm utilizes an implicit function continuous representation to decode image features, supporting super-resolution reconstruction up to 32 times, enabling the recovery of high-frequency details in a continuous manner and generating high-quality images. Experimental results show that compared to classical super-resolution models, our method has significant improvements in two key metrics, PSNR and SSIM, while effectively reducing artifacts and preserving more detailed information.