生成式对抗神经网络的多帧红外图像超分辨率重建  

Multiframe infrared image super-resolution reconstruction using generative adversarial networks

作　　者：李方彪 何昕[1] 魏仲慧[1] 何家维[1] 何丁龙 Li Fangbiao;He Xin;Wei Zhonghui;He Jiawei;He Dinglong(Changchun Institute of Optics,Fine Mechanics and Physics,Chinese Academy of Sciences,Changchun 130033,China;University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国科学院长春光学精密机械与物理研究所,吉林长春130033 [2]中国科学院大学,北京100049

出　　处：《红外与激光工程》2018年第2期17-24,共8页Infrared and Laser Engineering

基　　金：国家自然科学基金(61405191)

摘　　要：生成式对抗神经网络在约束图像生成表现出了巨大潜力,使得其适合运用于图像超分辨率重建。但是使用生成式对抗神经网络重建后的超分辨率图像存在过度平滑,缺少高频细节信息的缺点。针对单帧图像超分辨率重建方法不能有效利用图像序列间的时间-空间相关性的问题,提出了一种基于生成式对抗神经网络的多帧红外图像超分辨率重建方法(M-GANs)。首先,对低分辨率图像序列进行运动补偿;其次,使用权值表示卷积层对运动补偿后的图像序列进行权值转换计算;最后,将其输入生成式对抗重建网络,输出重建后的高分辨率图像。实验结果表明:文中方法在主观及客观评价中均优于当前代表性的超分辨率重建方法。Generative adversarial networks had shown promising potential in conditional image generation.It seemed that the GANs were particularly suitable for use in image super-resolution reconstruction.However,there was a shortcoming of excessive smoothness and lack of high frequency detail information for the reconstructed SR images by using GANs.Aiming at resolving the problem that the method of single image super-resolution reconstruction ignored the spatio-temporal relationship between image frames,a method of multiframe infrared image super-resolution reconstruction based on generative adversarial networks(M-GANs)was proposed in this paper.Firstly,motion compensation was proposed for registration low resolution image frames;Secondly,a weight representation convolutional layer was performed to calculate the weight transfer;Finally,the generative adversarial network was used to reconstruct the high resolution image.Experimental results demonstrate that the proposed method surpass current state-of-the-art performance of both subjective and objective evaluation.

关 键 词：超分辨率重建 深度学习 生成式对抗神经网络 红外成像 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]