Infrared imaging systems are highly susceptible to thermal radiation effects in complex environments, degrading optical imaging quality and reducing target contrast. Traditional correction methods, including iterative optimization based on physical models and deep learning approaches, face challenges in real-time applications due to high computational complexity and increased network depth. This paper proposes a lightweight and real-time deep learning model for thermal radiation correction. The model adopts an asymmetric encoder-decoder structure with a multi-output design to balance efficiency and real-time performance. A lightweight residual block (LRB) enhances feature learning while minimizing computation, while inter-stage feature fusion and channel-point attention modules improve multi-scale feature integration. Experimental results on PSNR, SSIM, MAE, and inference speed demonstrate that the proposed method outperforms existing approaches in computational efficiency and correction accuracy, making it highly suitable for real-time infrared imaging applications.