中文摘要：

在基于稀疏表示的幻觉脸重建过程中，由于冗余的过完备字典会降低稀疏编码的效率和精度，提出用紧的聚类子字典来表示人脸图像的不同结构对象。由高分辨率（high resolution， HR）/低分辨率（low resolution， LR）的人脸图像样本集进行K-均值聚类，为使紧的聚类子字典能够表达图像块的整体特征，对各聚类子集采用主成分分析（principal component analysis， PCA）方法构造字典。得到同构的HR/LR的聚类字典后，对于输入的LR人脸图像块，经自适应选择合适的子字典后，对稀疏编码添加正则化项，采用集中式稀疏编码，以使稀疏表示系数更逼近要重建的HR人脸图像块。由此稀疏表示系数与HR字典的线性组合得到HR人脸图像块，将此图像块与近似结果进行合成，从而得到最终的人脸图像。经仿真实验，并与其他的方法进行比较，实验结果验证了所提方法的有效性。

英文摘要：

In the processing of hallucinating faces reconstruction based on the sparse representation, aiming at the low sparse coding efficiency and precision caused by the redundant and over-complete dictionary, a method is proposed, in which the clustered compact sub dictionary is used to represent different objects of face images. The high resolution/low resolution （HR/LR） example face image patches are clustered by K means algorithm. To make the compact sub dictionary characterize the principal components of face image patches, the principal component analysis （PCA） algorithm is applied to learn sub-dictionary for each clustered dataset. After adaptively selecting the fitted sub dictionary for a given LR face image patch, a centralized sparse constraint is added to enforce the sparse coding coefficients to approximate the HR face image patch to be reconstruc ted. The HR face image patch can be reconstructed from the line combination of the sparse coding coefficients and HR sub dictionary. The final face image is synthesized by the HR patch and the smooth HR face image. With experiments on different face images and compared with other methods, the results demonstrate that the proposed method can hallucinate high quality faces in terms of both objective evaluation criteria and visual perception.