Image Super-Resolution via Double Sparsity Regularized Manifold Learning

Over the past few years, high resolutions have been desirable or essential, e. g., in online video systems, and therefore, much has been done to achieve an image of higher resolution from the corresponding low-resolution ones. This procedure of recovering/rebuilding is called single-image super-resolution (SR). Performance of image SR has been significantly improved via methods of sparse coding. That is to say, the image frame patch can be sparse linear combinations of basis elements. However, most of these existing methods fail to consider the local geometrical structure in the space of the training data. To take this crucial issue into account, this paper proposes a method named double sparsity regularized manifold learning (DSRML). DSRML can preserve the properties of the aforementioned local geometrical structure by employing manifold learning, e. g., locally linear embedding. Based on a large amount of experimental results, DSRML is demonstrated to be more robust and more effective than previous efforts in the task of single-image SR.