Image Super-Resolution via Local Self-Learning Manifold Approximation
This letter proposes a novel learning-based super-resolution method rooted in low dimensional manifold representations of high-resolution (HR) image-patch spaces. We exploit the input image and its different down-sampled scales to extract a set of training sample points using a min-max algorithm. A set of low dimensional tangent spaces is estimated from these samples using the l1 norm graph-based technique to cluster these samples into a set of manifold neighborhoods. The HR image is then reconstructed from these tangent spaces. Experimental results on standard images validate the effectiveness of the proposed method both quantitatively and perceptually.