高分辨率红外图像的获取受到了硬件性能的限制, 利用信号处理的方法实现红外图像的超分辨率重建可以有效地提高红外图像的分辨率.将基于深度学习的超分辨方法应用于红外图像, 实现了单帧红外图像的超分辨率重建, 获得了更好的评价结果.通过引入对抗训练的思想, 以及添加基于判别网络的损失函数分量, 提高了放大倍数的同时, 获得更好的高频细节恢复, 图像边缘锐化, 避免了超分辨率红外图像过于模糊.

Image processing makes super-resolution infrared image reconstruction effectively improve infrared images resolution, which breaks through hardware performance limits. Based on deep learning, super-resolution method is applied to infrared image, which enables the super-resolution reconstruction of single-frame infrared image. Thus, better evaluation results are acquired. Derived from adversarial thoughts, adding a loss function based on discriminant network can improve magnification, which can access to better high-frequency details of the restoration and can sharpen image edge and avoid blurred super-resolution infrared images.