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231223s2008 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2008.922421
|2 doi
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Jia, Kui
|e verfasserin
|4 aut
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| 245 |
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|a Generalized face super-resolution
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|c 2008
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 19.06.2008
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|a Date Revised 16.05.2008
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|a published: Print
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|a Citation Status MEDLINE
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|a Existing learning-based face super-resolution (hallucination) techniques generate high-resolution images of a single facial modality (i.e., at a fixed expression, pose and illumination) given one or set of low-resolution face images as probe. Here, we present a generalized approach based on a hierarchical tensor (multilinear) space representation for hallucinating high-resolution face images across multiple modalities, achieving generalization to variations in expression and pose. In particular, we formulate a unified tensor which can be reduced to two parts: a global image-based tensor for modeling the mappings among different facial modalities, and a local patch-based multiresolution tensor for incorporating high-resolution image details. For realistic hallucination of unregistered low-resolution faces contained in raw images, we develop an automatic face alignment algorithm capable of pixel-wise alignment by iteratively warping the probing face to its projection in the space of training face images. Our experiments show not only performance superiority over existing benchmark face super-resolution techniques on single modal face hallucination, but also novelty of our approach in coping with multimodal hallucination and its robustness in automatic alignment under practical imaging conditions
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|a Journal Article
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| 700 |
1 |
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|a Gong, Shaogang
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 17(2008), 6 vom: 01. Juni, Seite 873-86
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|x 1941-0042
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|g volume:17
|g year:2008
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|g month:06
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|u http://dx.doi.org/10.1109/TIP.2008.922421
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