Activating More Pixels in Image Super-Resolution Transformer

🤖 Plain-English Summary

Transformer-based methods have shown impressive performance in low-level vision tasks, such as image super-resolution. 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.

🔑 Key Findings

• 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).

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