Transformer-based models have achieved remarkable results in low-level vision tasks including image super-resolution (SR). However, early Transformer-based approaches that rely on self-attention within non-overlapping windows encounter challenges in acquiring global information. To activate more input pixels globally, hybrid attention models have been proposed. Moreover, training by solely minimizing pixel-wise RGB losses, such as L1, have been found inadequate for capturing essential high-frequency details. This paper presents two contributions: i) We introduce convolutional non-local sparse attention (NLSA) blocks to extend the hybrid transformer architecture in order to further enhance its receptive field. ii) We employ wavelet losses to train Transformer models to improve quantitative and subjective performance. While wavelet losses have been explored previously, showing their power in training Transformer-based SR models is novel. Our experimental results demonstrate that the proposed model provides state-of-the-art PSNR results as well as superior visual performance across various benchmark datasets.

这篇论文通过引入卷积非局部稀疏注意力块（NLSA）来扩展混合变压器架构，以进一步增强其感受野，并使用小波损失来训练变压器模型以改善定量和主观性能。实验结果表明，提出的模型在各种基准数据集上提供了最先进的峰值信噪比结果以及更好的视觉表现。

通过小波损失训练变压器模型提高单幅图像超分辨率的定量和视觉效果