Title :
Super-Resolution Using Manifold Learning
Author :
Karambelkar, Dattatray L. ; Kulkarni, P.J.
Author_Institution :
Dept. of Comput. Sci. & Eng., Walchand Coll. of Eng., Sangli, India
Abstract :
In this paper, a novel method for solving single image super-resolution problem is proposed. Objective is to recover a high resolution version of the given low resolution image. This method takes into account, a popular method of dimensionality reduction. It is believed that, small image patches in the low-resolution and high-resolution images form manifolds with similar local geometry in two distinct feature spaces. In locally linear embedding, focus is on reconstruction of a feature vector for a given patch using its neighbors in the feature space. This feature vector is used for the super resolution.
Keywords :
image reconstruction; image resolution; learning (artificial intelligence); dimensionality reduction; feature vector reconstruction; image patch; locally linear embedding method; manifold learning; single image super-resolution problem; Communication systems; Computational intelligence; LLE; Neighbor embedding; manifold learning;
Conference_Titel :
Computational Intelligence and Communication Networks (CICN), 2011 International Conference on
Conference_Location :
Gwalior
Print_ISBN :
978-1-4577-2033-8
DOI :
10.1109/CICN.2011.154
