Position-based face hallucination method

Authors:
Xiang Ma;Junping Zhang;Chun Qi
Affiliations:
School of Electronics & Information Engineering, Xi'an Jiaotong University, Xi'an, China;Department of Computer Science and Engineering, Fudan University, Shanghai, China;School of Electronics & Information Engineering, Xi'an Jiaotong University, Xi'an, China
Venue:
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Year:
2009

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Abstract

In this paper, we propose a novel face hallucination method to reconstruct a high-resolution face image from a low-resolution observation based on a set of high- and low-resolution local training image pairs. Instead of basing on probabilistic or manifold learning models, the proposed method synthesizes the high-resolution image patch using the same position image patches of training image pairs. A cost function is formulated to obtain the optimal weights of the training image position-patches and the high-resolution patches are reconstructed using the same weights. The final high-resolution facial image is formed by integrating the hallucinated patches. Experiments show that the proposed method without residue compensation generates higher-quality images than some methods.