Perceptually robust super-resolution through global feature awareness

Single-image super-resolution (SISR) plays an important role in a wide range of computer vision applications. Although recent SISR methods have achieved good results, there is no effective solution to the problem of high-frequency feature loss after super-resolution due to attentional drift caused by deep attention architectures. To address these problems, we propose a novel Transformer-based network called RFASR, which pioneers the design of a global attention boosting module (PixBoost) and a Self-Graph Attention (SGA) upsampling module. The former enhances feature representation by combining meta-learning and multi-spatial channel aggregation, while the latter improves the modeling of high-frequency details through graph-based attention in the up-sampling phase. Experimental results on multiple datasets show that RFASR has a very high reconstruction level while maintaining optimal recovery efficiency.