Face hallucination or super-resolution is an inverse problem which is underdetermined,and the compressive sensing(CS)theory provides an effective way of seeking inverse problem solutions.In this paper,a novel compress...Face hallucination or super-resolution is an inverse problem which is underdetermined,and the compressive sensing(CS)theory provides an effective way of seeking inverse problem solutions.In this paper,a novel compressive sensing based face hallucination method is presented,which is comprised of three steps:dictionary learning、sparse coding and solving maximum a posteriori(MAP)formulation.In the first step,the K-SVD dictionary learning algorithm is adopted to obtain a dictionary which can sparsely represent high resolution(HR)face image patches.In the second step,we seek the sparsest representation for each low-resolution(LR)face image paches input using the learned dictionary,super resolution image blocks are obtained from the sparsest coefficients and dictionaries,which then are assembled into super-resolution(SR)image.Finally,MAP formulation is introduced to satisfy the consistency restrictive condition and obtain the higher quality HR images.The experimental results demonstrate that our approach can achieve better super-resolution faces compared with other state-of-the-art method.展开更多
提出了一种基于多分辨率的中心对称局部二阶微分模式(multi-resolution center-symmetric local derivative pattern,CS-MLDP)的人脸图像识别算法。CS-LDP算法仅仅从4个方向上提取图像纹理特征,不能充分描述图像纹理细节特征。在CS-LDP...提出了一种基于多分辨率的中心对称局部二阶微分模式(multi-resolution center-symmetric local derivative pattern,CS-MLDP)的人脸图像识别算法。CS-LDP算法仅仅从4个方向上提取图像纹理特征,不能充分描述图像纹理细节特征。在CS-LDP算法的基础上,通过插值运算得到更多的近邻点,再提取局部二阶微分特征,从而得到多分辨率CS-LDP算法(CS-MLDP)。展开更多
文摘Face hallucination or super-resolution is an inverse problem which is underdetermined,and the compressive sensing(CS)theory provides an effective way of seeking inverse problem solutions.In this paper,a novel compressive sensing based face hallucination method is presented,which is comprised of three steps:dictionary learning、sparse coding and solving maximum a posteriori(MAP)formulation.In the first step,the K-SVD dictionary learning algorithm is adopted to obtain a dictionary which can sparsely represent high resolution(HR)face image patches.In the second step,we seek the sparsest representation for each low-resolution(LR)face image paches input using the learned dictionary,super resolution image blocks are obtained from the sparsest coefficients and dictionaries,which then are assembled into super-resolution(SR)image.Finally,MAP formulation is introduced to satisfy the consistency restrictive condition and obtain the higher quality HR images.The experimental results demonstrate that our approach can achieve better super-resolution faces compared with other state-of-the-art method.
文摘提出了一种基于多分辨率的中心对称局部二阶微分模式(multi-resolution center-symmetric local derivative pattern,CS-MLDP)的人脸图像识别算法。CS-LDP算法仅仅从4个方向上提取图像纹理特征,不能充分描述图像纹理细节特征。在CS-LDP算法的基础上,通过插值运算得到更多的近邻点,再提取局部二阶微分特征,从而得到多分辨率CS-LDP算法(CS-MLDP)。
