In recent years, Lightweight image super-resolution technology has achieved good performance. However, many models struggle to effectively capture and process global information, leading to problems such as loss of detail and unnatural texture in reconstructed images. To solve these problems, we propose a Multi-Dimensional Information Awareness Residual Network (MIAN), which adopts a lightweight design to ensure efficient image reconstruction performance. Firstly, our MIAN effectively aggregates multi-scale context information through multi-layer channel distillation blocks (MCDB), which helps to extract important features layer by layer and reconstruct high-frequency details more accurately. Secondly, we design hierarchical spatial amplification attention (HSAA) to further enhance attention to key areas of the image and significantly improve the ability to capture and reconstruct details by layering the importance of different areas. Thirdly, we propose rapid channel perception attention (RCPA), which makes the network more focused on the useful information of the current task by optimizing the information interaction between feature channels. Finally, we introduce lightweight deepwise global self-attention (LDGA), which can identify and utilize similar features in a wide range and effectively keep the details and texture information in the reconstruction process. Extensive experiments show that our MIAN significantly improves the quality of image super-resolution, reduces the parameters and calculation cost, and achieves state-of-the-art super-resolution reconstruction performance.