摘要
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,w hich breaks through hardw are performance limits. Based on deep learning,super-resolution method is applied to infrared image,w hich 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 netw ork can improve magnification,w hich can access to better high-frequency details of the restoration and can sharpen image edge and avoid blurred super-resolution infrared images.
作者
邵保泰
汤心溢
金璐
李争
SHAO Bao-Tai1,2,3, TANG Xin-Yi1,3, JIN Lu1,2,3, LI Zheng1,3.(1. Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China; 2. University of Chinese Academy of Sciences, Beijing 100049, China; 3. CAS Key Laboratory of hffrared System Detection and Imaging Technology, Shanghai Institute of Technical Physics, Shanghai 200083, Chin)
出处
《红外与毫米波学报》
SCIE
EI
CAS
CSCD
北大核心
2018年第4期427-432,共6页
Journal of Infrared and Millimeter Waves
基金
国家十三五国防预研项目(Jzx2016-0404/Y72-2)
上海市现场物证重点实验室基金资助项目(2017xcwzk08)~~
