Iterated neighbor-embeddings for image super-resolution

We propose an exemplar-based super-resolution algorithm based on sparsity constrained neighbor-embeddings of local image patches. We extract exemplar patch pairs from as little as the given low-resolution image, and we rely on local geometric similarities of low-and high-resolution patch spaces. While sparsely coding the local geometry with a greedy patch selection method, we refine our solution by iteratively updating the obtained high-resolution image. We finally apply an adaptive back-projection to ensure the global consistency. Our experimental results indicate promising performance on synthesizing natural looking textures and sharp edges when compared to other super-resolution methods from the literature.