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.

翻译：单图像超分辨率（SISR）算法的一个重要发展方向是提升算法效率。近年来，高效超分辨率（SR）的研究聚焦于通过改进深度小核卷积来降低模型复杂度并提升效率，但这导致感受野较小。大核卷积虽能获得大感受野从而显著提升图像质量，但其计算成本过高。为改善高效超分辨率重建的细节质量，我们提出了一种对称视觉注意力网络（SVAN），通过引入大感受野实现改进。该网络将大核卷积分解为三种不同卷积操作的组合，并结合注意力机制构成对称大核注意力块（SLKAB）。该注意力块通过卷积组合中感受野的尺寸形成具有瓶颈结构的对称块，作为SVAN的基础组件高效提取深度特征。我们的网络在最小化参数数量的同时获得大感受野，并提升模型的感知能力。实验结果表明，所提出的SVAN仅需现有SOTA方法约30%的参数即可获得高质量的超分辨率重建结果。