Single Image Super-Resolution Algorithm based on Fixed-Point Multi-Model Gaussian Process Regression

Abstract

To solve the problem of large amount of computation in matrix inversion of Gaussian process regression model, a super-resolution algorithm based on local Gaussian process regression of fixed point multi-model is proposed. Firstly, the training samples are classified by Gaussian mixture model, and image patches are randomly selected as fixed points in each type of training samples, and its K nearest neighbor patch are searched. Secondly, local Gaussian process regression model by using its low-mid-frequency components and the corresponding high frequency components. Again, low resolution test image patch is classified, only the K nearest neighbor test image patch is searched during reconstruction. And then, find the nearest fixed point in each kind of image patch, and use the local Gaussian process regression model based on the fixed point to predict its corresponding high-frequency component. Finally, corresponding high frequency information is predicted by using this trained model, which improves the reconstruction efficiency. Experimental results have demonstrated that the proposed algorithm is superior in both quantitative and qualitative aspects against other algorithms.