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- Title
HRM graph constrained dictionary learning for face image super-resolution.
- Authors
Huang, Kebin; Hu, Ruimin; Jiang, Junjun; Han, Zhen; Wang, Feng
- Abstract
Sparse coding based face image Super-Resolution (SR) approaches have received increasing amount of interest recently. However, most of the existing sparse coding based approaches fail to consider the geometrical structure of the face space, as a result, artificial effects on reconstructed High Resolution (HR) face images come into being. In this paper, a novel sparse coding based face image SR method is proposed to reconstruct a HR face image from a Low Resolution (LR) observation. In training stage, it aims to get a more expressive HR-LR dictionary pair for certain input LR patch. The intrinsic geometric structure of training samples is incorporated into the sparse coding procedure for dictionary learning. Unlike the existing SR methods which use the graph constructed in LR Manifold (LRM) as regularization term, the proposed method uses graph constructed in HR Manifold (HRM) as regularization term. In reconstruction stage, K selection mean constrains is used in l convex optimization, aiming at finding an optimal weight for HR face image patch reconstruction. Experimental results on both simulation and real world images suggest that our proposed one achieves better quality when compared with other state-of-the-art methods.
- Subjects
HIGH resolution imaging; IMAGE compression; SPARSE graphs; ENCYCLOPEDIAS &; dictionaries; GEOMETRICAL constructions
- Publication
Multimedia Tools & Applications, 2017, Vol 76, Issue 2, p3139
- ISSN
1380-7501
- Publication type
Article
- DOI
10.1007/s11042-015-3215-z