Breaking the Limitation of Manifold Analysis for Super-Resolution of Facial Images
A novel method for robust super-resolution of face images is proposed in this paper. Face super-resolution is a particular interest in video surveillance where face images have typically very low-resolution quality and there is a need to apply face enhancement or super-resolution algorithms. In this paper, we apply a manifold learning method which has hardly been used for super-resolution. A manifold is a natural generalization of a Euclidean space to a locally Euclidean space. Manifold learning algorithms are more powerful than other pattern recognition methods which analyze a Euclidean space because they can reveal the underlying nonlinear distribution of the face space; however, there are some practical problems which prevent these algorithms from being applied to super-resolution. Almost all of the manifold learning methods cannot generate mapping functions for new test images which are absent from a training set. Another factor is that super-resolution seeks to recover a high-dimensional image from a lower-dimensional one while manifold learning methods perform the exact opposite as they are applied to dimensionality reduction. In this paper, we break the limitation of applying manifold learning methods for face super-resolution by proposing a novel method using locality preserving projections (LPP).