关键词: Contourlet域, 隐马尔可夫树, 超分辨率复原, 遥感图像

Abstract: 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.

Key words: Contourlet domain, Hidden Markov Tree (HMT), super resolution restoration, remote sensing image