NON-LOCAL SIMILARITY DICTIONARY LEARNING BASED FACE SUPER-RESOLUTION

被引：0
|
作者：
Liao, Haibin [1 ]
Dai, Wenhua [1 ]
Zhou, Qianjin [2 ]
Liu, Bo [3 ]
机构：
[1] HuBei Univ Sci & Technol, Sch Comp Sci & Technol, Xianning 437100, Peoples R China
[2] Wuhan Univ, Sch Elect Informat, Wuhan 430072, Peoples R China
[3] Chang Jiang Inst Survey Planning Design & Res, Wuhan, Peoples R China
来源：
2014 12TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING (ICSP) | 2014年
关键词：
Super-Resolution; Face Recognition; Dictionary Learning; Linear Combination; Non-local Similarity; IMAGE SUPERRESOLUTION; RECOGNITION;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Face Super-Resolution (SR) is the process of producing a high-resolution face image from a set of low-resolution face images. Most existing dictionary learning based algorithms suffer a high degree of computational complexity and noise sensitivity. To solve this problem, we proposed a novel face SR method based on non-local similarity and multi-scale linear combination (NLS-MLC). Multi-scale linear combination consistency is proved under different resolutions. Experimental results show that the proposed SR method is more robust to noise and computationally efficient.
引用
收藏
页码：88 / 93
页数：6
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