深度反投影网络在可见光图像的超分辨率重建中具有优异的表现，本文探索将深度反投影网络应用到红外图像超分辨率重建中。针对红外图像对比度低、图像质量不高的特点，在深度反投影网络框架上作如下改进：在上采样模块之前添加串联层，将前一次的下采样输出和原始低分辨率预处理图像串联作为上采样模块的输入，以此提高网络获取图像高频信息的能力，增强生成图像的细节信息。实验结果证明，本文算法较改进前能够得到细节更加丰富、视觉效果更加良好的红外超分辨率重建图像。

Deep back-projection networks have excellent performance in the super-resolution reconstruction of visual images. This paper explores the application of deep back-projection networks to the super-resolution reconstruction of infrared images. In view of the characteristics of low infrared image contrast and low image quality, the following improvements were made in the framework of the deep back-projection network: adding a concatenation layer before the upsampling module, cascading the previous downsampling output and the original low-resolution preprocessed image as the input of the upsampling module. This was designed to improve the network's ability to obtain high-frequency information of the image and enhance the detail of the generated image. The experimental results proved that the proposed algorithm could create infrared super-resolution reconstructed images with richer details and improved visual effects.