Keywords: 
• 超分辨率 /
• 图像序列 /
• 分辨率增强 /
• 自适应正则化 /
• 图像重建

Abstract: The images reconstructed by tranditional regularization super-resolution often have over smoothing or different artifacts residue.The cause of artifacts is analyzed by super-resolution reconstruction model.To improve the disadvantage of tra-ditional methods,this paper proposes an adaptive regularization algorithm based on image region information,the original image is divided into smooth and non-smooth regions by the information,each type of region use different type of prior model as con-straints.Considering the characteristics of human vision,regional information is used to achieve adaptive regularization parameter selection.Experiment results indicate that the proposed algorithm can improve the quality of reconstructed image with better arti-facts smoothing and details preserving than tranditional method and regularization with single prior model,which provides a theo-retical reference to enhance infrared and visible light image super-resolution reconstruction quality.