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Face hallucination based on sparse local-pixel structure

Li, Yongchao; Cai, Cheng; Qiu, Guoping; Lam, Kin-Man


Yongchao Li

Cheng Cai

Professor of Visual Information Processing

Kin-Man Lam


In this paper, we propose a face-hallucination method, namely face hallucination based on sparse local-pixel structure. In our framework, a high resolution (HR) face is estimated from a single frame low resolution (LR) face with the help of the facial dataset. Unlike many existing face-hallucination methods such as the from local-pixel structure to global image super-resolution method (LPS-GIS) and the super-resolution through neighbor embedding, where the prior models are learned by employing the least-square methods, our framework aims to shape the prior model using sparse representation. Then this learned prior model is employed to guide the reconstruction process. Experiments show that our framework is very flexible, and achieves a competitive or even superior performance in terms of both reconstruction error and visual quality. Our method still exhibits an impressive ability to generate plausible HR facial images based on their sparse local structures.


Li, Y., Cai, C., Qiu, G., & Lam, K. (2014). Face hallucination based on sparse local-pixel structure. Pattern Recognition, 47(3), 1261-1270.

Journal Article Type Article
Acceptance Date Sep 16, 2013
Online Publication Date Sep 24, 2013
Publication Date 2014-03
Deposit Date Jul 31, 2018
Publicly Available Date Jul 31, 2018
Journal Pattern Recognition
Print ISSN 0031-3203
Electronic ISSN 0031-3203
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 47
Issue 3
Pages 1261-1270
Keywords Face hallucination; Sparse local-pixel structure; Super-resolution; Sparse representation
Public URL
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