Abstract
Transformer architectures have demonstrated remarkable performance in image super-resolution (SR). However, existing Transformer-based models generally suffer from insufficient local feature modeling, weak feature representation capabilities, and unreasonable loss function design, especially when reconstructing high-resolution (HR) images, where the restoration of fine details is poor. To address these issues, we propose a novel SR model, Parallel Attention Recursive Generalization Transformer (PARGT) in this study, which can effectively capture the fine-grained interactions between local features of the image and other regions, resulting in clearer and more coherent generated details. Specifically, we introduce the Parallel Local Self-attention (PL-SA) module, which enhances local features by parallelizing the Shift Window Pixel Attention Module (SWPAM) and Channel-Spatial Shuffle Attention Module (CSSAM). In addition, we introduce a new type of feed-forward network called Spatial Fusion Convolution Feed-forward Network (SFCFFN) for multi-scale information fusion. Finally, we optimize the reconstruction of high-frequency details by incorporating a Stationary Wavelet Transform (SWT). Experimental results on several challenging benchmark datasets demonstrate the superiority of our PARGT over state-of-the-art image SR models, showcasing the effectiveness of combining a parallel attention mechanism with a multi-scale feed-forward network for SR tasks. The code will be available at https://github.com/hgzbn/PARGT .