Recently there have been significant advances in image up scaling or image super-resolution based on a dictionary of low and high resolution exemplars. The running time of the methods is often ignored despite the fact that it is a critical factor for real applications. This paper proposes fast super-resolution methods while making no compromise on quality. First, we support the use of sparse learned dictionaries in combination with neighbor embedding methods. In this case, the nearest neighbors are computed using the correlation with the dictionary atoms rather than the Euclidean distance. Moreover, we show that most of the current approaches reach top performance for the right parameters. Second, we show that using global collaborative coding has considerable speed advantages, reducing the super-resolution mapping to a precomputed projective matrix. Third, we propose the anchored neighborhood regression. That is to anchor the neighborhood embedding of a low resolution patch to the nearest atom in the dictionary and to precompute the corresponding embedding matrix. These proposals are contrasted with current state-of-the-art methods on standard images. We obtain similar or improved quality and one or two orders of magnitude speed improvements.

In this paper we propose the use of nonuniformly resized image patch exemplars for solving low level vision problems like denoising and super-resolution. While patch-based methods have been shown to be successful for several such applications, these methods have so far assumed uniform sizes for image patches. In this paper we address this restriction. We use an integral image representation for efficient computation of the matching cost for variable-sized patches. We show that nonuniform image patch exemplars are useful in improving classic techniques for nonlocal means-based denoising and example-based superresolution. We provide refinement cues to further improve the patch size estimation. This method can be adopted for a large number of related methods and applications due to its simplicity and generality.

Currently two evaluation methods of super-resolution (SR) techniques prevail: The objective Peak Signal to Noise Ratio (PSNR) and a qualitative measure based on manual visual inspection. Both of these methods are sub-optimal: The latter does not scale well to large numbers of images, while the former does not necessarily reflect the perceived visual quality. We address these issues in this paper and propose an evaluation method based on image classification. We show that perceptual image quality measures like structural similarity are not suitable for evaluation of SR methods. On the other hand a systematic evaluation using large datasets of thousands of real-world images provides a consistent comparison of SR algorithms that corresponds to perceived visual quality. We verify the success of our approach by presenting an evaluation of three recent super-resolution algorithms on standard image classification datasets.