Activating More Pixels in Image Super-Resolution Transformer

TL;DR

This paper introduces a Hybrid Attention Transformer that combines global and local attention mechanisms, along with cross-window interaction and pre-training strategies, to significantly improve image super-resolution performance.

Contribution

The paper proposes a novel Hybrid Attention Transformer with overlapping cross-attention and pre-training, enhancing pixel utilization and surpassing state-of-the-art results in image super-resolution.

Findings

Outperforms existing methods by over 1dB in PSNR.

Effectively activates more input pixels for better reconstruction.

Demonstrates scalability and robustness of the proposed modules.

Abstract

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 https://github.com/XPixelGroup/HAT.