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2007
IEEE

Breaking the Limitation of Manifold Analysis for Super-Resolution of Facial Images

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Breaking the Limitation of Manifold Analysis for Super-Resolution of Facial Images
A novel method for robust super-resolution offace 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 ofthe 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-dimen...
Sung Won Park, Marios Savvides
Added 05 Jun 2010
Updated 05 Jun 2010
Type Conference
Year 2007
Where ICASSP
Authors Sung Won Park, Marios Savvides
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