CAJ | 학술논문

在基于邻域嵌入人脸图像的超分辨率重建算法中，训练和重建均在特征空间进行，因此，特征选择对算法性能具有较大影响。另外，算法模型对重建权重未加限定，导致负数权重出现而产生过拟合效应，使得重建人脸图像质量衰退。考虑到人脸图像的特征选择以及权重符号限定的重要作用，该文提出一种基于2维主成分分析(2D- PCA)特征描述的非负权重邻域嵌入人脸超分辨率重建算法。首先将人脸图像分成若干子块，利用K均值聚类获得图像子块的局部视觉基元，并利用得到的局部视觉基元对图像子块分类。然后，利用2D-PCA 对每一类人脸图像子块提取特征，并建立高、低分辨率样本库。最后，在重建过程中使用新的非负权重求解方法求取权重。仿真实验结果表明，相比其他基于邻域嵌入人脸超分辨率重建方法，所提算法可有效提高权重的稳定性，减少过拟合效应，其重建人脸图像具有较好的主客观质量。
재기우린역감입인검도상적초분변솔중건산법중，훈련화중건균재특정공간진행，인차，특정선택대산법성능구유교대영향。령외，산법모형대중건권중미가한정，도치부수권중출현이산생과의합효응，사득중건인검도상질량쇠퇴。고필도인검도상적특정선택이급권중부호한정적중요작용，해문제출일충기우2유주성분분석(2D- PCA)특정묘술적비부권중린역감입인검초분변솔중건산법。수선장인검도상분성약간자괴，이용K균치취류획득도상자괴적국부시각기원，병이용득도적국부시각기원대도상자괴분류。연후，이용2D-PCA 대매일류인검도상자괴제취특정，병건립고、저분변솔양본고。최후，재중건과정중사용신적비부권중구해방법구취권중。방진실험결과표명，상비기타기우린역감입인검초분변솔중건방법，소제산법가유효제고권중적은정성，감소과의합효응，기중건인검도상구유교호적주객관질량。
In neighbor embedding based face hallucination, the training and reconstruction processes are performed in the feature space, thus the feature selection is important. In addition, there is no constraint specified for the signs of the weights generated in neighbor embedding algorithm, which leads to over-fitting and degradation of the recovered face images. Considering the importance of feature selection and the constraints of weights, a novel neighbor embedding face hallucination method is proposed based on non-negative weights and Two-Dimensional Principal Component Analysis (2D-PCA) features. First, the face images are partitioned into patches, and the local visual primitives are obtained by k-means clustering algorithm. The face image patches are classified with the local visual primitives generated before. Second, the feature of face image patches is captured with 2D-PCA, and the low and high dictionary is established. Finally, a novel non-negative weights solution method is used to obtain the weights. The experiment results show that the weights computed by the proposed method have more stable behavior and obviously less over-fitting phenomenon, furthermore, the recovery face images have better subjective and objective quality.