摘要
提出了一种新的基于学习的人脸图像超分辨率重构算法,利用高分辨率图像和低分辨率图像的拓扑结构相似性,将现有的低分辨率人脸图像在低分辨率人脸图像字典中展开,在保持系数不变的同时将字典换为高分辨率人脸图像字典,最终得到待重构的高分辨率人脸图像.在系数估计时,使用主成分分析的方法,同时加入了最小全变分作为约束,算法充分利用了不同人脸图像之间的相似性和人脸图像本身的内部相关性.实验结果表明,结果既保持了对原有图像的忠实性,又比较适合人眼观察.
A learning based super-resolution algorithm for reconstructing face image was proposed.Considering that the similarity of the structures between high resolution（HR） image and corresponding low resolution（LR） image when unfolded on the platform of image library,the input LR image on the built face dictionary for reconstruction was decomposed.Then,the face dictionary of LR images is replaced by corresponding one of HR images with same coefficients.In the coefficients evaluation step,the principal component analysis（PCA） method is used and the total variation（TV） is added as the constraint.The experiment results show that the proposed algorithm could well preserve the faith to the original image and the reconstructed face image is more suitable to be observed by human eyes.
出处
《北京理工大学学报》
EI
CAS
CSCD
北大核心
2012年第4期386-389,共4页
Transactions of Beijing Institute of Technology
基金
国家自然科学基金资助项目(60772066)
关键词
人脸图像超分辨率
主成分分析
全变分
约束
image super-resolution reconstruction
principal component analysis（PCA）
total variation
constrain
