Efficient Image Super-Resolution via Symmetric Visual Attention Network
Chengxu Wu Qinrui Fan Shu Hu Xi Wu Xin Wang Jing Hu
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.