Super-resolution reconstruction method based on adaptive-regularization

The images reconstructed by traditional 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 traditional 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 constraints. 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 artifacts smoothing and details preserving than traditional method and regularization with single prior model, which provides a theoretical reference to enhance infrared and visible light image super-resolution reconstruction quality.