Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91303
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Title: Face hallucination based on cluster consistent dictionary learning
Authors: Li, M
He, X
Lam, KM 
Zhang, K
Jing, J
Issue Date: Oct-2021
Source: IET image processing, Oct. 2021, v. 15, no. 12, p. 2841-2853
Abstract: Face hallucination is a super-resolution technique specially designed to reconstruct high-resolution faces from low-resolution faces. Most state-of-the-art algorithms leverage position-patch prior knowledge of human faces to better super-resolve face images. However, most of them assume the training face dataset is sufficiently large, well cropped or aligned. This paper, proposes a novel example-based face hallucination method, based on cluster consistent dictionary learning with the assumption that human faces have similar facial structures. In this method, the paired face image patches are firstly labelled as face areas including eyes, nose, mouth and other parts, as well as non-face areas without requiring the training face images cropped and aligned. Then, the training patches are clustered according their labels and textures. The cluster consistent dictionary is learned to represent the low-resolution patches and the high-resolution patches. Finally, the high-resolution patches of the input low-resolution face image can be efficiently generated by using the adjusted anchored neighbourhood regression. As utilizing the labelled facial parts prior knowledge, the proposed method represents more details in the reconstruction. Experimental results demonstrate that the authors' algorithm outperforms many state-of-the-art techniques for face hallucination under different¬†datasets.
Publisher: Institution of Engineering and Technology
Journal: IET image processing 
ISSN: 1751-9659
EISSN: 1751-9667
DOI: 10.1049/ipr2.12269
Rights: ¬© 2021 The Authors. IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology
This is an open access article under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
The following publication Li, M., He, X., Lam, K. M., Zhang, K., & Jing, J. (2021). Face hallucination based on cluster consistent dictionary learning. IET Image Processing is available at https://doi.org/10.1049/ipr2.12269
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