An important development direction in the Single-Image Super-Resolution
(SISR) algorithms is to improve the efficiency of the algorithms. Recently,
efficient Super-Resolution (SR) research focuses on reducing model complexity
and improving efficiency through improved deep small kernel convolution,
leading to a small receptive field. The large receptive field obtained by large
kernel convolution can significantly improve image quality, but the
computational cost is too high. To improve the reconstruction details of
efficient super-resolution reconstruction, we propose a Symmetric Visual
Attention Network (SVAN) by applying large receptive fields. The SVAN
decomposes a large kernel convolution into three different combinations of
convolution operations and combines them with an attention mechanism to form a
Symmetric Large Kernel Attention Block (SLKAB), which forms a symmetric
attention block with a bottleneck structure by the size of the receptive field
in the convolution combination to extract depth features effectively as the
basic component of the SVAN. Our network gets a large receptive field while
minimizing the number of parameters and improving the perceptual ability of the
model. The experimental results show that the proposed SVAN can obtain
high-quality super-resolution reconstruction results using only about 30% of
the parameters of existing SOTA methods.

通过应用大感受野，我们提出了一种称为 Symmetric Visual Attention Network (SVAN) 的方法来改进高效超分辨率重建，减少了算法参数的数量并提高了模型的感知能力。实验结果表明，我们的方法只使用了现有 SOTA 方法约 30% 的参数，却能获得高质量的超分辨率重建结果。