Activating More Pixels in Image Super-Resolution Transformer

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.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 However, we find that these networks can only utilize a limited spatial range of input information through attribution analysis.
• 2 This implies that the potential of Transformer is still not fully exploited in existing networks.
• 3 In order to activate more input pixels for better reconstruction, we propose a novel Hybrid Attention Transformer (HAT).

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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