Convolutional networks dominate many machine vision fields. Nevertheless, a significant drawback of the convolution operation is that it only operates in the local region, so it lacks global information. Self-attention has become the latest technology for capturing long-range interactions, but it is mainly used for generative modeling and sequence modeling tasks. Using self-attention to tackle super-resolution as a substitute for convolution is considered. Therefore, augmented global attention convolution (AGAC) is proposed as an alternative to convolution to use self-attention for super-resolution. The proposed augmented global attention convolution can capture global context to produce more realistic super-resolution results. Due to the most existing works that have not exploited position information, a two-dimensional relative self-attention mechanism is proposed to enhance self-attention. To deal with the super-resolution task, the authors come up with an augmented global attention convolutional network (AGAN) to enhance the convolution operator with the self-attention mechanism through concatenating the convolution pattern map with the generated set of feature maps. Many experiments and analyses are conducted to demonstrate that the proposed model surpasses the advanced models with comparable parameters and performance.