Single Image Super-resolution Reconstruction with Neural Network and Gaussian Process Regression

In this paper, we propose a novel single image super-resolution (SR) reconstruction framework based on artificial neural network (ANN) and Gaussian process regression (GPR). The ANN is used for SR reconstruction, and the GPR is used for correction. The new framework combines multiple reconstruction approaches including deep learning and sparse representation from a local dictionary. The main contribution is enhancing the reconstruction performance utilizing the image compressed features with respect to other state of art single image SR approaches in terms of both visual perception and quantitative assessment.