CAJ | 학술논문

通过Contourlet域对遥感图像进行超分辨复原,采用了具有的更好方向性和各向异性特点的Contottrlet系数作为特征表示,并通过最小欧氏距离进行全局的匹配选择.根据匹配的高频细节信息分布特点,引入了隐马尔可夫树(HMT)模型对遥感图像的Contourlet系数建模,运用期望最大(EM)算法对其进行参数估计,并结合贝叶斯估计原理,对叠加后的Contourlet系数进行修复、反变换后,最终完成了对低分辨率遥感图像的超分辨率复原.
통과Contourlet역대요감도상진행초분변복원,채용료구유적경호방향성화각향이성특점적Contottrlet계수작위특정표시,병통과최소구씨거리진행전국적필배선택.근거필배적고빈세절신식분포특점,인입료은마이가부수(HMT)모형대요감도상적Contourlet계수건모,운용기망최대(EM)산법대기진행삼수고계,병결합패협사고계원리,대첩가후적Contourlet계수진행수복、반변환후,최종완성료대저분변솔요감도상적초분변솔복원.
This paper presented a Contourlet-based super resolution for remote sensing images, which adopted Contourlet coefficients as the features. It described a better degree of directionality and anisotropy, and used the smallest Euclidean distance as the computed feature by global searching. According to the distributions of the found coefficients in finer scale, the Hidden Markov Tree (HMT) model was introduced to the remote sensing images in Contourlet domain. And the Expectation Maximization (EM) algorithm was applied to estimate the parameters of the HMT model. With the parameters, the Contourlet coefficients were renewed by using Bayesian estimation theory. Finally, the super resolution restorationfor remote sensing images has achieved better effect.