HiT-SR: Hierarchical Transformer for Efficient Image Super-Resolution

• URL: http://arxiv.org/abs/2407.05878v1
• Date: Mon, 8 Jul 2024 12:42:10 GMT
• Title: HiT-SR: Hierarchical Transformer for Efficient Image Super-Resolution
• Authors: Xiang Zhang, Yulun Zhang, Fisher Yu,
• Abstract summary: We present a strategy to convert transformer-based SR networks to hierarchical transformers (HiT-SR) Specifically, we first replace the commonly used fixed small windows with expanding hierarchical windows to aggregate features at different scales. Considering the intensive computation required for large windows, we further design a spatial-channel correlation method with linear complexity to window sizes.
• Score: 70.52256118833583
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transformers have exhibited promising performance in computer vision tasks including image super-resolution (SR). However, popular transformer-based SR methods often employ window self-attention with quadratic computational complexity to window sizes, resulting in fixed small windows with limited receptive fields. In this paper, we present a general strategy to convert transformer-based SR networks to hierarchical transformers (HiT-SR), boosting SR performance with multi-scale features while maintaining an efficient design. Specifically, we first replace the commonly used fixed small windows with expanding hierarchical windows to aggregate features at different scales and establish long-range dependencies. Considering the intensive computation required for large windows, we further design a spatial-channel correlation method with linear complexity to window sizes, efficiently gathering spatial and channel information from hierarchical windows. Extensive experiments verify the effectiveness and efficiency of our HiT-SR, and our improved versions of SwinIR-Light, SwinIR-NG, and SRFormer-Light yield state-of-the-art SR results with fewer parameters, FLOPs, and faster speeds ($\sim7\times$).

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