This paper introduces a lightweight infrared image super-resolution (SR) model called the Lightweight Information Split Network (LISN). The model consists of four main components: shallow feature extraction, deep feature extraction, dense feature fusion, and high-resolution infrared image reconstruction. A key innovation is the Lightweight Information Split Block (LISB), which employs a sequential process to extract hierarchical features and aggregates them based on their relevance. By integrating channel splitting and shift operations, the LISB achieves a balance between enhanced SR performance and a lightweight framework. Comprehensive experiments show that LISN outperforms state-of-the-art methods in terms of SR quality and model complexity, making it suitable for practical deployment in resource-constrained infrared imaging applications. The model uses a combination of CNNs and shift technologies, along with channel splitting operations and attention mechanisms, to enhance the effectiveness of the super-resolution process. The LISN achieves high PSNR and SSIM values on the CVC-09-1K dataset, demonstrating its superior performance in infrared image super-resolution. The model is efficient, with a low parameter count and reduced computational demands, making it suitable for deployment on devices with limited resources. The results show that LISN achieves significant improvements in PSNR and SSIM compared to other methods, while maintaining a low parameter count and efficient computation. The model's effectiveness is further supported by ablation studies, which show that the LISB contributes significantly to the model's performance. The model's architecture is efficient, with a low parameter count and reduced computational demands, making it suitable for deployment on devices with limited resources. The model's performance is validated through extensive experiments on public datasets, demonstrating its effectiveness in infrared image super-resolution.This paper introduces a lightweight infrared image super-resolution (SR) model called the Lightweight Information Split Network (LISN). The model consists of four main components: shallow feature extraction, deep feature extraction, dense feature fusion, and high-resolution infrared image reconstruction. A key innovation is the Lightweight Information Split Block (LISB), which employs a sequential process to extract hierarchical features and aggregates them based on their relevance. By integrating channel splitting and shift operations, the LISB achieves a balance between enhanced SR performance and a lightweight framework. Comprehensive experiments show that LISN outperforms state-of-the-art methods in terms of SR quality and model complexity, making it suitable for practical deployment in resource-constrained infrared imaging applications. The model uses a combination of CNNs and shift technologies, along with channel splitting operations and attention mechanisms, to enhance the effectiveness of the super-resolution process. The LISN achieves high PSNR and SSIM values on the CVC-09-1K dataset, demonstrating its superior performance in infrared image super-resolution. The model is efficient, with a low parameter count and reduced computational demands, making it suitable for deployment on devices with limited resources. The results show that LISN achieves significant improvements in PSNR and SSIM compared to other methods, while maintaining a low parameter count and efficient computation. The model's effectiveness is further supported by ablation studies, which show that the LISB contributes significantly to the model's performance. The model's architecture is efficient, with a low parameter count and reduced computational demands, making it suitable for deployment on devices with limited resources. The model's performance is validated through extensive experiments on public datasets, demonstrating its effectiveness in infrared image super-resolution.