HRM graph constrained dictionary learning for face image super-resolution

详细信息    查看全文

• 作者：Kebin Huang ; Ruimin Hu ; Junjun Jiang ; Zhen Han…
• 关键词：Super ; resolution ; HRM graph regularization ; Sparse coding ; Dictionary learning
• 刊名：Multimedia Tools and Applications
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：76
• 期：2
• 页码：3139-3162
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems;
• 出版者：Springer US
• ISSN：1573-7721
• 卷排序：76

文摘

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 l₁ 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.