Currently, with the development of deep learning techniques and large models, designing efficient network models has become one of the hot topics in research. In the field of image super-resolution reconstruction, although deep convolutional neural networks have made significant progress, the increase in network complexity has led to an increase in computational overhead and excessive consumption of computational resources on high-performance devices (e.g., GPU). To address this issue, a network for image super-resolution reconstruction based on partial convolution (Pconv) and an improved agent attention mechanism is proposed. By reducing redundant computations and memory access, the network can more effectively extract spatial features, significantly reducing computational complexity while maintaining superior performance. Through experiments comparing recent methods on public datasets in terms of performance metrics, the proposed network model demonstrates leading results in objective quantitative measures, promising to provide a more efficient and viable solution for image super-resolution reconstruction tasks.