Super-Resolution for Face Image with an Improved K-NN Search Strategy
Recently, neighbor embedding based face super-resolution（SR） methods have shown the ability for achieving high-quality face images, those methods are based on the assumption that the same neighborhoods are preserved in both low-resolution（LR） training set and high-resolution（HR） training set. Howeve...
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| Published in | 中国通信 no. 4; pp. 151 - 161 |
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| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
2016
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| Online Access | Get full text |
| ISSN | 1673-5447 |
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| Summary: | Recently, neighbor embedding based face super-resolution（SR） methods have shown the ability for achieving high-quality face images, those methods are based on the assumption that the same neighborhoods are preserved in both low-resolution（LR） training set and high-resolution（HR） training set. However, due to the ＂one-to-many＂ mapping between the LR image and HR ones in practice, the neighborhood relationship of the LR patch in LR space is quite different with that of the HR counterpart, that is to say the neighborhood relationship obtained is not true. In this paper, we explore a novel and effective re-identified K-nearest neighbor（RIKNN） method to search neighbors of LR patch. Compared with other methods, our method uses the geometrical information of LR manifold and HR manifold simultaneously. In particular, it searches K-NN of LR patch in the LR space and refines the searching results by re-identifying in the HR space, thus giving rise to accurate K-NN and improved performance. A statistical analysis of th |
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| Bibliography: | Recently, neighbor embedding based face super-resolution（SR） methods have shown the ability for achieving high-quality face images, those methods are based on the assumption that the same neighborhoods are preserved in both low-resolution（LR） training set and high-resolution（HR） training set. However, due to the ＂one-to-many＂ mapping between the LR image and HR ones in practice, the neighborhood relationship of the LR patch in LR space is quite different with that of the HR counterpart, that is to say the neighborhood relationship obtained is not true. In this paper, we explore a novel and effective re-identified K-nearest neighbor（RIKNN） method to search neighbors of LR patch. Compared with other methods, our method uses the geometrical information of LR manifold and HR manifold simultaneously. In particular, it searches K-NN of LR patch in the LR space and refines the searching results by re-identifying in the HR space, thus giving rise to accurate K-NN and improved performance. A statistical analysis of th |
| ISSN: | 1673-5447 |