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HMANet: Hybrid Multi-Axis Aggregation Network for Image Super-Resolution
May 9, 2024, 4:45 a.m. | Shu-Chuan Chu, Zhi-Chao Dou, Jeng-Shyang Pan, Shaowei Weng, Junbao Li
cs.CV updates on arXiv.org arxiv.org
Abstract: Transformer-based methods have demonstrated excellent performance on super-resolution visual tasks, surpassing conventional convolutional neural networks. However, existing work typically restricts self-attention computation to non-overlapping windows to save computational costs. This means that Transformer-based networks can only use input information from a limited spatial range. Therefore, a novel Hybrid Multi-Axis Aggregation network (HMA) is proposed in this paper to exploit feature potential information better. HMA is constructed by stacking Residual Hybrid Transformer Blocks(RHTB) and Grid Attention …
aggregation arxiv cs.cv hybrid image network resolution type
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