Image super-resolution aims to reconstruct high-resolution images from their low-resolution counterparts, enhancing visual quality and recovering high-frequency details. Although deep learning-based methods have significantly advanced SR performance, existing approaches still face three main challenges: First, neglect or ineffective utilization of information across different dimensions. Second, ineffective aggregation of multi-scale information often leads to unnatural visual artifacts,especially around image edges and complex texture regions. Then, difficulty in balancing local detail preservation with global contextual understanding. In this paper, we propose a SR model that integrates both spatial and channel attention mechanisms to extract and aggregate features across dimensions more effectively. We introduce an Adaptive Interaction Module to fuse features within each block and further enhance the network’s representational capacity. Additionally, we introduce an alternating structure that leverages dense attention for fine-grained local details and sparse attention for long-range dependencies, enabling a broader receptive field. Experimental results demonstrate that our model achieves superior reconstruction quality with improved perceptual realism, validating the effectiveness of the proposed dual-attention aggregation strategy.