Enriched CNN-Transformer Feature Aggregation Networks for Super-Resolution

Jinsu Yoo; Taehoon Kim; Sihaeng Lee; Seung Hwan Kim; Honglak Lee; Tae Hyun Kim

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arXiv · 2203.07682

Enriched CNN-Transformer Feature Aggregation Networks for Super-Resolution

Jinsu Yoo·Taehoon Kim·Sihaeng Lee·Seung Hwan Kim·Honglak Lee·Tae Hyun Kim

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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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Jinsu Yoo, Taehoon Kim, Sihaeng Lee, Seung Hwan Kim, Honglak Lee, Tae Hyun Kim. 2022-03-15. Enriched CNN-Transformer Feature Aggregation Networks for Super-Resolution. https://arxiv.org/abs/2203.07682

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