Swift Parameter-free Attention Network for Efficient Super-Resolution
Cheng Wan Hongyuan Yu Zhiqi Li Yihang Chen Yajun Zou Yuqing Liu Xuanwu Yin Kunlong Zuo
Single Image Super-Resolution (SISR) is a crucial task in low-level computer
vision, aiming to reconstruct high-resolution images from low-resolution
counterparts. Conventional attention mechanisms have significantly improved
SISR performance but often result in complex network structures and large
number of parameters, leading to slow inference speed and large model size. To
address this issue, we propose the Swift Parameter-free Attention Network
(SPAN), a highly efficient SISR model that balances parameter count, inference
speed, and image quality. SPAN employs a novel parameter-free attention
mechanism, which leverages symmetric activation functions and residual
connections to enhance high-contribution information and suppress redundant
information. Our theoretical analysis demonstrates the effectiveness of this
design in achieving the attention mechanism's purpose. We evaluate SPAN on
multiple benchmarks, showing that it outperforms existing efficient
super-resolution models in terms of both image quality and inference speed,
achieving a significant quality-speed trade-off. This makes SPAN highly
suitable for real-world applications, particularly in resource-constrained
scenarios. Notably, our model attains the best PSNR of 27.09 dB, and the test
runtime of our team is reduced by 7.08ms in the NTIRE 2023 efficient
super-resolution challenge. Our code and models are made publicly available at
\url{https://github.com/hongyuanyu/SPAN}.