Transformer-based methods have shown impressive performance in low-level
vision tasks, such as image super-resolution. However, we find that these
networks can only utilize a limited spatial range of input information through
attribution analysis. This implies that the potential of Transformer is still
not fully exploited in existing networks. In order to activate more input
pixels for better reconstruction, we propose a novel Hybrid Attention
Transformer (HAT). It combines both channel attention and window-based
self-attention schemes, thus making use of their complementary advantages of
being able to utilize global statistics and strong local fitting capability.
Moreover, to better aggregate the cross-window information, we introduce an
overlapping cross-attention module to enhance the interaction between
neighboring window features. In the training stage, we additionally adopt a
same-task pre-training strategy to exploit the potential of the model for
further improvement. Extensive experiments show the effectiveness of the
proposed modules, and we further scale up the model to demonstrate that the
performance of this task can be greatly improved. Our overall method
significantly outperforms the state-of-the-art methods by more than 1dB. Codes
and models are available at this https URL

为了更好的利用 Transformer 的潜力以进行图片重构，在融合通道注意力和基于窗口的自注意机制的优势的基础上提出了一种新型的 Hybrid Attention Transformer 模型。此外，还引入了重叠交叉注意力模块并采用相同任务的预训练策略，拓展模型的能力。实验证明这个模型在图片超分辨率方面的表现优于现有方法超过 1dB。