摘要
基于超完备稀疏表示理论,并根据人脸图像的特征,提出一种基于局部约束的人脸图像超分辨率重构算法。该算法首先通过样本训练出一对高、低分辨率相关联的冗余字典;再根据局部范围内人脸图像的相关性,重构出高分辨率图像;最后对图像进行全局优化。为验证算法的有效性,本文利用ORL标准图像库进行了对比实验,实验结果表明,该算法能够有效提高峰值信噪比,同时能够更好地恢复人脸图像的高频信息,有一定的实用价值。
Based on the overcomplete sparse representation theory,a face image super-resolution reconstruction methodbased on local constraint is proposed.First,a pair of associated redundant dictionary,including high-resolution （HR）and low-resolution （LR）,are calculated through sample training.Based on the correlation in local scope of face im-age,the high-resolution image is reconstructed.Then,the image is optimized globally.To verify the validity of the algo-rithm,the contrast experiment is made by using ORL standard image,the results show that the algorithm can work wellin the actual conditions,and can recover the high-resolution information perfectly.
出处
《激光与红外》
CAS
CSCD
北大核心
2014年第2期217-221,共5页
Laser & Infrared
基金
江西省教育厅科研项目(No.GJJ13762)资助
关键词
超分辨率重构
稀疏表示
局部约束
人脸图像
K-SVD
super-resolution reconstruction
sparse representation
local constraint
face image
K-SVD
