Single image super resolution via internal gradient similarity
A novel super resolution (SR) method based on internal gradient similarity.A detailed investigation on constructing images from gradients.Study on internal and external statistics (general and class specific) for image SR.High quality results compared with other state of the art SR algorithms. Image super resolution aims to reconstruct a high resolution image from one or multiple low resolution images which is an essential operation in a variety of applications. Due to the inherent ambiguity for super resolution, it is a challenging task to reconstruct clear, artifacts free edges while still preserving rich and natural textures. In this paper, we propose a novel, straightforward, and effective single image super resolution method based on internal across scale gradient similarity. The low resolution gradients are first upsampled and then fed into an optimization framework to construct the final high resolution output. The proposed approach is able to synthesize natural high frequency texture details and maintain clean edges even under large scaling factors. Experimental results demonstrate that out method outperforms existing single image super resolution techniques. We further evaluate the super resolution performance when both internal statistics and external statistics are adopted. It is demonstrated that generally, internal statistics are sufficient for single image super resolution.
J. Vis. Commun. Image Represent.