Single image super resolution with high resolution dictionary

Abstract

Image super resolution (SR) is a technique to estimate or synthesize a high resolution (HR) image from one or several low resolution (LR) images. This paper proposes a novel framework for single image super resolution based on sparse representation with high resolution dictionary. Unlike the previous methods, the training set is constructed from the HR images instead of HR-LR image pairs. Due to this property, there is no need to retrain a new dictionary when the zooming factor changed. Given a testing LR image, the patch-based representation coefficients and the desired image are estimated alternately through the use of dynamic group sparsity, the fidelity term and the non-local means regularization. Experimental results demonstrate the effectiveness of the proposed algorithm.