Aiming at the problem that the super-resolution effect on different low-resolution images of existing super-resolution methods has large difference, this paper proposed a new single image super-resolution method based on sparse Bayesian estimation. In this method, it regarded the single image super-resolution problem as a regression problem. It used the Kronecker pulse functions as the regression basis functions, and obtained the optimal sparse solution of the specific prediction by combining the local information and global information of the image. It used the Bayesian method to estimate the weights, thereby reconstructing the super-resolution image. The experimental results show that this method can obtain high average peak signal to noise ratio, small variance and less time-consuming, when carried out on 14 testing images for single image super-resolution. It proves that this method has good super-resolution effect, strong adaptability, and high efficiency.

[2019-02-05] Printed Article /doc/2017.09.0959/v2