The use of deep convolutional neural networks (CNNs) for image super-resolution (SR) from low-resolution (LR) input has achieved remarkable reconstruction performance with the utilization of residual structures and visual attention mechanisms. However, existing single image super-resolution (SISR) methods with deeper network architectures can encounter representational bottlenecks in CNN-based networks and neglect model efficiency in model statistical inference. To solve these issues, in this paper, we design a channel hourglass residual structure (CHRS) and explore an efficient channel attention (ECA) mechanism to extract more representative features and ease the computational burden. Specifically, our CHRS, consisting of several nested residual modules, is developed to learn more discriminative representations with fewer model parameters, and the ECA is presented to efficiently capture local cross-channel interaction by subtly applying 1D convolution. Finally, we propose an efficient residual attention network (ERAN), which not only fully learns more representative features but also pays special attention to network learning efficiency. Extensive experiments demonstrate that our ERAN achieves certain improvements in model performance and implementation efficiency compared to other previous state-of-the-art methods.