Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/91303
| 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 |
| Appears in Collections: | Journal/Magazine Article |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| ipr2.12269.pdf | 2.13 MB | Adobe PDF | View/Open |
Access
View full-text via PolyU eLinks 
Page views
13
Last Week
0
0
Last month
Citations as of May 28, 2023
Downloads
4
Citations as of May 28, 2023
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.