Hybrid Transformer and CNN Attention Network for Stereo Image Super-resolution
| Autor: | Cheng, Ming, Ma, Haoyu, Ma, Qiufang, Sun, Xiaopeng, Li, Weiqi, Zhang, Zhenyu, Sheng, Xuhan, Zhao, Shijie, Li, Junlin, Zhang, Li |
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| Rok vydání: | 2023 |
| Předmět: | |
| DOI: | 10.48550/arxiv.2305.05177 |
| Popis: | 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. Comment: 10 pages, 3 figures, accepted by CVPR workshop 2023 |
| Databáze: | OpenAIRE |
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