Rich CNN-Transformer Feature Aggregation Networks for Super-Resolution

• URL: http://arxiv.org/abs/2203.07682v2
• Date: Wed, 16 Mar 2022 11:52:47 GMT
• Title: Rich CNN-Transformer Feature Aggregation Networks for Super-Resolution
• Authors: Jinsu Yoo, Taehoon Kim, Sihaeng Lee, Seung Hwan Kim, Honglak Lee, Tae Hyun Kim
• Abstract summary: Recent vision transformers along with self-attention have achieved promising results on various computer vision tasks. We introduce an effective hybrid architecture for super-resolution (SR) tasks, which leverages local features from CNNs and long-range dependencies captured by transformers. Our proposed method achieves state-of-the-art SR results on numerous benchmark datasets.
• Score: 50.10987776141901
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent vision transformers along with self-attention have achieved promising results on various computer vision tasks. In particular, a pure transformer-based image restoration architecture surpasses the existing CNN-based methods using multi-task pre-training with a large number of trainable parameters. In this paper, we introduce an effective hybrid architecture for super-resolution (SR) tasks, which leverages local features from CNNs and long-range dependencies captured by transformers to further improve the SR results. Specifically, our architecture comprises of transformer and convolution branches, and we substantially elevate the performance by mutually fusing two branches to complement each representation. Furthermore, we propose a cross-scale token attention module, which allows the transformer to efficiently exploit the informative relationships among tokens across different scales. Our proposed method achieves state-of-the-art SR results on numerous benchmark datasets.

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