Noise Face Image Hallucination via Data-Driven Local Eigentransformation

Face hallucination refers to inferring an High-Resolution HR face image from the input Low-Resolution LR one. It plays a vital role in LR face recognition by both manual and computer. The eigentransformation method based on Principal Component Analysis PCA, which represents face image as a linear combination of the eigenfaces, has attracted considerable interests because of its simplicity and effectiveness. However, the face image observed is in a high-dimensional non-linear space, whose statistical properties cannot be captured by the PCA based linear modeling method. To this end, in this paper we advance a Data-driven Local Eigentransformation DLE method for face hallucination by exploiting the local geometry structure of data manifold and learning a specified eigentransformation model for each observed image. Experimental results show the effectiveness of the proposed approach for hallucinating face images especially with noise.

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