Transformer-based methods have shown impressive performance in image
restoration tasks, such as image super-resolution and denoising. 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 restoration, we propose a new Hybrid
Attention Transformer (HAT). It combines both channel attention and
window-based self-attention schemes, thus making use of their complementary
advantages. 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 further exploit the potential of the
model for further improvement. Extensive experiments have demonstrated the
effectiveness of the proposed modules. We further scale up the model to show
that the performance of the SR task can be greatly improved. Besides, we extend
HAT to more image restoration applications, including real-world image
super-resolution, Gaussian image denoising and image compression artifacts
reduction. Experiments on benchmark and real-world datasets demonstrate that
our HAT achieves state-of-the-art performance both quantitatively and
qualitatively. Codes and models are publicly available at
this https URL

Transformer-based methods have limitations in utilizing input information, so a Hybrid Attention Transformer (HAT) is proposed to improve restoration tasks by combining channel attention and window-based self-attention schemes.