Abstract: Addressing the issues of limited feature information extraction and low feature utilization in existing image super-resolution reconstruction methods, we propose a Multi-Scale Dual Attention （MSDA） approach. Firstly， this method employs multi-scale feature extraction blocks to capture feature information from different scales of the input image. Subsequently， a dual attention mechanism is introduced to enable the network to rapidly focus on high-frequency regions in the images， while utilizing skip connections to mitigate feature information loss during deep network propagation. Lastly， a dropout layer is employed to balance the importance of feature channels， preventing network co-adaptation， and enhancing the model’s generalization capability. Experimental results on public test datasets， including Set5， Set14， BSD100， Urban100， and Manga109， demonstrate that MSDA achieves superior performance by generating images with enhanced high-frequency information， enriched texture details， and a perceptual resemblance to the original high-resolution images.

Key words: super-resolution, multi-scale features, dual attention, jump connection