Face super-resolution via multilayer locality-constrained iterative neighbor embedding and intermediate dictionary learning

Face super-resolution via multilayer locality-constrained iterative neighbor embedding and intermediate dictionary learning

Based on the assumption that low-resolution (LR) and high-resolution (HR) manifolds are locally isometric, the neighbor embedding super-resolution algorithms try to preserve the geometry (reconstruction weights) of the LR space for the reconstructed HR space, but neglect the geometry of the original...

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Veröffentlicht in:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 23(2014), 10 vom: 27. Okt., Seite 4220-31
1. Verfasser: Jiang, Junjun (VerfasserIn)
Weitere Verfasser: Hu, Ruimin, Wang, Zhongyuan, Han, Zhen
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2014
Zugriff auf das übergeordnete Werk:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Schlagworte:Journal Article Research Support, Non-U.S. Gov't
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520 |a Based on the assumption that low-resolution (LR) and high-resolution (HR) manifolds are locally isometric, the neighbor embedding super-resolution algorithms try to preserve the geometry (reconstruction weights) of the LR space for the reconstructed HR space, but neglect the geometry of the original HR space. Due to the degradation process of the LR image (e.g., noisy, blurred, and down-sampled), the neighborhood relationship of the LR space cannot reflect the truth. To this end, this paper proposes a coarse-to-fine face super-resolution approach via a multilayer locality-constrained iterative neighbor embedding technique, which intends to represent the input LR patch while preserving the geometry of original HR space. In particular, we iteratively update the LR patch representation and the estimated HR patch, and meanwhile an intermediate dictionary learning scheme is employed to bridge the LR manifold and original HR manifold. The proposed method can faithfully capture the intrinsic image degradation shift and enhance the consistency between the reconstructed HR manifold and the original HR manifold. Experiments with application to face super-resolution on the CAS-PEAL-R1 database and real-world images demonstrate the power of the proposed algorithm 
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700 1 |a Wang, Zhongyuan  |e verfasserin  |4 aut 
700 1 |a Han, Zhen  |e verfasserin  |4 aut 
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