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 Blocks(GAB). On the one side, RHTB combines channel attention and
self-attention to enhance non-local feature fusion and produce more attractive
visual results. Conversely, GAB is used in cross-domain information interaction
to jointly model similar features and obtain a larger perceptual field. For the
super-resolution task in the training phase, a novel pre-training method is
designed to enhance the model representation capabilities further and validate
the proposed model's effectiveness through many experiments. The experimental
results show that HMA outperforms the state-of-the-art methods on the benchmark
dataset. We provide code and models at this https URL

本文提出了一种新颖的混合多轴聚合网络（HMA），通过叠加残差混合 Transformer 块（RHTB）和网格注意力块（GAB），在超分辨率视觉任务中充分利用特征潜力信息，并通过实验验证了该模型的有效性。