NDLI: A Face Hallucination Algorithm via an LLE Coefficients Prior Model

Content Provider	IET Digital Library
Author	Li, Xiaoguang Jia, Guangheng Li, Jiafeng Zhuo, Li
Abstract	In the most of exiting Local linear embedding (LLE)-based image super-resolution methods, a Low resolution (LR) image can be represented as a linear combination of LR training samples. In these methods, the combination coefficients of the LR image are directly used to estimate the High resolution (HR) image. However, experimental results show that the LR-LLE coefficients are different from the corresponding HR-LLE coefficients. To bridge the gap between LR and HR images, a novel LLEbased face hallucination algorithm is proposed. An LLE coefficients prior model is introduced to reduce the coefficient errors. In this prior model, the LLE coefficients of the interpolated LR face image are used to constraint the reconstructed coefficients. Experimental results show that the proposed method can provide improved performance over the compared methods.
Starting Page	1234
Ending Page	1240
Page Count	7
ISSN	10224653
Volume Number	27
e-ISSN	20755597
Issue Number	Issue 6, Nov (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/cje/27/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/cje.2018.09.011
Journal	Chinese Journal of Electronics
Publisher Date	2018-11-01
Access Restriction	Open
Rights Holder	© Chinese Institute of Electronics
Subject Keyword	Coefficient Error Combination Coefficient Computer Vision And Image Processing Technique Experimental Result Face Recognition High Resolution Image HR-LLE Coefficient Image Reconstruction Image Resolution Interpolated LR Face Image Linear Combination LLE Coefficient Prior Model Local Linear Embedding-based Image Super-resolution Method Low Resolution Image LR Image LR Training Sample LR-LLE Coefficient Novel LLE-based Face Hallucination Algorithm Optical, Image And Video Signal Processing Reconstructed Coefficient Statistics
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Electrical and Electronic Engineering

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