An Example-Based Two-Step Face Hallucination Method through Coefficient Learning

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:
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Year:
2009

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Abstract

Face hallucination is to reconstruct a high-resolution face image from a low-resolution one based on a set of high- and low-resolution training image pairs. This paper proposes an example-based two-step face hallucination method through coefficient learning. Firstly, the low-resolution input image and the low-resolution training images are interpolated to the same high-resolution space. Minimizing the square distance between the interpolated low-resolution input and the linear combination of the interpolated training images, the optimal coefficients of the interpolated training images are estimated. Then replacing the interpolated training images with the corresponding high-resolution training images in the linear combination formula, the result of first step is obtained. Furthermore, a local residue compensation scheme based on position is proposed to better recover high frequency information of face. Experiments demonstrate that our method can synthesize distinct high-resolution faces.