摘要
提出了红外超分辨率重建系统以获取高分辨率红外数据。首先,根据红外图像获取过程建立了数学模型,讨论了降采样、模糊、运动以及高斯噪声对红外系统的影响;在非退化特征提取的基础上提出了基于特征的亚像素配准算法,其根据所得到的非退化特征应用归一化均方根误差来估计两帧之间的亚像素位移。然后,分析了传统全变分因子在高分辨重建时的不足并对其进行改进;利用区域划分将图像划分为平滑区域和细节区域,并根据区域的不同情况自适应全变分因子,从而使细节区域不至于过平滑。最后,利用MM(Majorization Minimixation)算法对合成的低分辨率红外图像和真实红外图像进行了超锐度重建。与同类相关算法的比较实验显示:所提算法亚像素配准最大误差为0.09pixel,重建后的红外图像质量优于其他同类算法。所提算法可以对低分辨红外图像序列进行有效重建,具有配准精度高、重建图像细节丰富等特点,可应用于各种红外成像系统。
An infrared super resolution reconstruction system was proposed to acquire high resolution infrared images.A mathematical model was established according to the procedure of image acquisition.The effect of down-sampling,blurring,motion,and Gussian noise on the infrared system were discussed.Then,a non-degradation feature based sub-pixel motion estimation method was proposed.On the basis of obtained non-degradation,the normalized root of mean square was utilized to estimate the sub-pixel motion between two frames.Furthermore,drawbacks of the conventional total variation factor were analyzed and improved when it was applied in the reconstruction procedure.The region division method was used to divide the image into smooth regions and detail regions,then the new variational factor was able to adaptive to different regions according to their characteristics,and the detail regions could not be over-smoothed.Finally,the experiments on both synthetic and real infrared image sequences were performed by MM（Majorization Minimization）.The results indicate that the maximum error of proposed algorithm is 0.09 pixel and the quality of the reconstructed image is better than those of the other algorithms.The proposed algorithm has higher sub-pixel registration accuracyand rich image details,and is able to reconstruct the sequence of low resolution infrared images efficiently.It is suitable for various infrared applications.
出处
《光学精密工程》
EI
CAS
CSCD
北大核心
2015年第10期2989-2996,共8页
Optics and Precision Engineering
基金
国家自然科学基金资助项目(No.61171155
No.61571364)
陕西省自然科学基金资助项目(No.2012JM8010)
关键词
分辨率增强
红外图像
超分辨率重建
亚像素
全变分
resolution enhancement
infrared image
super resolution reconstruction
sub-pixel
total variation
