As a representative low-level vision problem, image super-resolution (SR) aims to reconstruct the high-resolution image from its low-resolution counterpart. For a long time, the analysis of SR tasks is based on the whole image, while little works observe the input partition. In this paper, we find that the restoration quality of a certain position is inseparable from its surrounding image background. This phenomenon provides us a new perspective to explain the networks by splitting the input image. We construct a new hybrid dataset, of which the foreground and background contain only one kind of texture information. And then, we prove that the similar background could benefit the network restoration. By analyzing similarity and difference between the attention mechanism and the traditional CNN network, we show that the attention structure could help the network focus on long-range effective information. Moreover, a data enhancement method to improve the network final performance and potential future works are also proposed.