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Image hallucination using neighbor embedding over visual primitive manifolds

Authors Fan, Wei HKUST affiliated (currently or previously)
Yeung, Dit-Yan View this author's profile
Issue Date 2007
Source IEEE Conference on Computer Vision & Pattern Recognition (CVPR), CVPR'07, Minneapolis, Minnesota, USA, 17 - 22 June 2007, Code 70350\2007, p. 244-250, Article number 4270026
Summary In this paper, we propose a novel learning-based method for image hallucination, with image super-resolution being a specific application that we focus on here. Given a low-resolution image, its underlying higher-resolution details are synthesized based on a set of training images. In order to build a compact yet descriptive training set, we investigate the characteristic local structures contained in large volumes of small image patches. Inspired by recent progress in manifold learning research, we take the assumption that small image patches in the low-resolution and high-resolution images form manifolds with similar local geometry in the corresponding image feature spaces. This assumption leads to a super-resolution approach which reconstructs the feature vector corresponding to an image patch by its neighbors in the feature space. In addition, the residual errors associated with the reconstructed image patches are also estimated to compensate for the information loss in the local averaging process. Experimental results show that our hallucination method can synthesize higher-quality images compared with other methods.
ISSN 1063-6919
ISBN 978-1-4244-1179-5
Rights © 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Language English
Format Conference paper
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