Face hallucination using OLPP and Kernel Ridge Regression

Abstract

Generally face images may be visualized as points drawn on a low-dimensional manifold embedded in high-dimensional ambient space. Many dimensionality reduction techniques have been used to learn this manifold. Orthogonal locality preserving projection (OLPP) is one among them which aims to discover the local structure of the manifold and produces orthogonal basis functions. In this paper, we present a two-step patch based algorithm for face superresolution. In first step a MAP based framework is used to obtain high resolution patch from its low resolution counterpart where the face subspace is learnt using OLPP. To enhance the quality of the image further, we propose a method which uses kernel ridge regression to learn the relation between low and high resolution residual patches. Experimental results show that our approach can reconstruct high quality face images.