结合位置先验与稀疏表示的单帧人脸图像超分辨率算法

提出了一种结合位置先验与稀疏表示的人脸图像超分辨率算法，可对单帧输入的低分辨率人脸图像基于训练集进行超分辨率重建。利用压缩感知理论中的信号分解方法，〖BP(〗明确哪些方法更好〖BP)〗，将稀疏表示与人脸位置先验信息相结合，使用经过分类的超完备冗余字典，来分别稀疏逼近输入信号的块向量结构。利用最佳的K项原子，线性组合重建出高分辨率图像块。最后按照图像块最初在人脸的位置，将它们拼接为整体人脸。在CAS-PEAL-R1人脸图库上的实验结果表明，该算法使用相对较少的原子，就可以重建出质量较好的高分辨率人脸图像。

Face hallucination based on position prior and sparse representation

A face hallucination method based on sparse representation and position prior was proposed,which can obtain the enlargement of a single low-resolution input.Some perspectives of compressed sensing were applied to the method.The high-and low-resolution over-complete atoms were classified according to different positions of face.The low-resolution face image inputs were approximated by the sparse linear combination of the over-complete atoms which were classified.The sparse coefficients were obtained to reconstruct the high-resolution data of certain position.According to their original positions,the generated patches were integrated into a global face.The experimental results illustrate that the proposed method can generate satisfying high-resolution face image using fewer atoms compared to other methods.