Enriched CNN-Transformer Feature Aggregation Networks for Super-Resolution
- Authors
- Yoo, Jinsu; Kim, Taehoon; Lee, Sihaeng; Kim, Seung Hwan; Lee, Honglak; Kim, Tae Hyun
- Issue Date
- Jan-2023
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Algorithms: Computational photography; image and video synthesis; Low-level and physics-based vision
- Citation
- Proceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023, pp.4945 - 4954
- Indexed
- SCOPUS
- Journal Title
- Proceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023
- Start Page
- 4945
- End Page
- 4954
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/182538
- DOI
- 10.1109/WACV56688.2023.00493
- ISSN
- 0000-0000
- Abstract
- Recent transformer-based super-resolution (SR) methods have achieved promising results against conventional CNN-based methods. However, these approaches suffer from essential shortsightedness created by only utilizing the standard self-attention-based reasoning. In this paper, we introduce an effective hybrid SR network to aggregate enriched features, including local features from CNNs and long-range multi-scale dependencies captured by transformers. Specifically, our network comprises transformer and convolutional branches, which synergetically complement each representation during the restoration procedure. Furthermore, we propose a cross-scale token attention module, allowing the transformer branch to exploit the informative relationships among tokens across different scales efficiently. Our proposed method achieves state-of-the-art SR results on numerous benchmark datasets.
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