The inherent trade-off between spatial and angular resolution in light field (LF) imaging leads to the necessity of LF image super-resolution (SR). While existing methods have validated that combining both spatial and angular information can significantly improve LF image SR, they often fail to effectively merge these two components or model global relationships among sub-aperture images, limiting reconstruction quality. In this paper, we propose LF-ATnet, a novel super-resolution network based on attention mechanisms. Our approach introduces an angular incorporation module and a spatial-angular locally-enhanced self-attention module to capture local angular and global spatial features, respectively. A dual-branch structure is employed to facilitate efficient feature fusion and interaction. In the reconstruction phase, channel attention blocks and stacked multi-distillation blocks are applied to hierarchically modulate features, ensuring the recovery of fine details. Experimental results on public datasets demonstrate that LF-ATnet achieves superior performance in both visual quality and quantitative metrics compared to existing methods. Our method effectively combine spatial and angular information and reconstruct high-resolution LF images with rich textures.