DyLKANet: A Lightweight Dynamic Distillation Network for Remote Sensing Image Super-Resolution Based on Large-Kernel Attention
Bing He, Bingchao Wang, Ying Fu, Xi Ma, Liqun Sun · Electronics · 2025
Lightweight remote sensing image super-resolution methods aim to enhance image resolution and recover fine details through lightweight neural networks. However, current lightweight methods still suffer from poor performance and unattractive details. DyLKANet introduces a novel lightweight architecture that utilizes a multi-level feature integration strategy to enhance information exchange between context-aware and large kernel attention mechanisms. The network comprises two core modules: the feature distillation and enhancement block for efficient feature extraction, and the context-aware attention-based feature fusion module for capturing global interdependencies. Experiments conducted on the UCMerced, AID, and DIV2K datasets reveal that DyLKANet achieves comparable performance while maintaining a low parameter count and computational complexity. Taking the 2× upscaling results on the UCMerced dataset as an example, specifically, DyLKANet improves PSNR by 0.212–3.544 dB, SSIM by 0.005–0.038, and reduces parameters by 18.79–95.46%. DyLKANet reduces FLops by 7.25–82.63%, making it a promising solution for remote sensing image super-resolution tasks in resource-constrained environments.