Enhancing Image Super-Resolution Models with Shift Operations and Hybrid Attention Mechanisms

This study proposes an optimized approach to address the high computational demands and significant GPU memory consumption commonly associated with Transformer-based models. Building upon the HAT framework, a shallow feature extraction module is enhanced to improve local feature representation, thereby achieving a better balance between computational efficiency and model performance. Furthermore, inspired by self-supervised learning (SSL) techniques and incorporating shift operations, the proposed method effectively reduces both the number of parameters and the overall computational load. The resulting ISR-SHA model is trained and evaluated on the DF2K dataset, achieving approximately a 30% reduction in FLOPs and parameter count compared to the original HAT model, with only marginal declines in PSNR (0.02) and SSIM (0.0006). Experimental results confirm that ISR-SHA outperforms most existing super-resolution models in terms of performance while significantly enhancing computational efficiency without compromising output quality.