Super resolution using a single image dictionary

To perform super resolution of low resolution images, state-of-the-art methods are based on learning a pair of low-resolution and high-resolution dictionaries from multiple images. These trained dictionaries are used to replace patches in low-resolution image with appropriate matching patches from the high-resolution dictionary. In this paper we propose using a single common image as dictionary, in conjunction with approximate nearest neighbour fields (ANNF) to perform super resolution (SR). By using a common source image, we are able to bypass the learning phase and also able to reduce the dictionary from a collection of hundreds of images to a single image. By adapting recent developments in ANNF computation, to suit super-resolution, we are able to perform much faster and accurate SR than existing techniques. To establish this claim, we compare the proposed algorithm against various state-of-the-art algorithms, and show that we are able to achieve better and faster reconstruction without any training.