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.
@article{arxiv.2203.07682,
title = {Enriched CNN-Transformer Feature Aggregation Networks for Super-Resolution},
author = {Jinsu Yoo and Taehoon Kim and Sihaeng Lee and Seung Hwan Kim and Honglak Lee and Tae Hyun Kim},
journal= {arXiv preprint arXiv:2203.07682},
year = {2022}
}