STSR: An Efficient Model for Super-Resolution Reconstruction
Xinhui Liu · 2025
STSR is an efficient and lightweight super-resolution model built upon the Swin Transformer architecture. By utilizing the hierarchical window-based self-attention mechanism of the Swin Transformer, STSR effectively captures global contextual information while ensuring high computational efficiency. To further enhance its performance and reduce model complexity, we incorporate lightweight design elements and residual structures. These improvements not only facilitate better gradient flow but also significantly boost the recovery of high-frequency details. Experimental results on the DIV2K dataset show that STSR delivers exceptional performance, significantly reducing both parameter count and computational costs.