Hybrid Transformer and CNN Attention Network for Stereo Image
  Super-resolution
Ming Cheng Haoyu Ma Qiufang Ma Xiaopeng Sun Weiqi Li Zhenyu Zhang Xuhan Sheng Shijie Zhao Junlin Li Li Zhang
Multi-stage strategies are frequently employed in image restoration tasks.
While transformer-based methods have exhibited high efficiency in single-image
super-resolution tasks, they have not yet shown significant advantages over
CNN-based methods in stereo super-resolution tasks. This can be attributed to
two key factors: first, current single-image super-resolution transformers are
unable to leverage the complementary stereo information during the process;
second, the performance of transformers is typically reliant on sufficient
data, which is absent in common stereo-image super-resolution algorithms. To
address these issues, we propose a Hybrid Transformer and CNN Attention Network
(HTCAN), which utilizes a transformer-based network for single-image
enhancement and a CNN-based network for stereo information fusion. Furthermore,
we employ a multi-patch training strategy and larger window sizes to activate
more input pixels for super-resolution. We also revisit other advanced
techniques, such as data augmentation, data ensemble, and model ensemble to
reduce overfitting and data bias. Finally, our approach achieved a score of
23.90dB and emerged as the winner in Track 1 of the NTIRE 2023 Stereo Image
Super-Resolution Challenge.