Face Image Enhancement via Principal Component Analysis
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This paper investigates face image enhancement based on the principal component analysis (PCA). We first construct two types of training samples: one consists of some high-resolution face images, and the other includes the low resolution images obtained via smoothed and down-sampling process from the first set. These two corresponding sets form two different image spaces with different resolutions. Second, utilizing the PCA, we obtain two eigenvector sets which form the vector basis for the high resolution space and the low resolution space, and a unique relationship between them is revealed. We propose the algorithm as follows: first project the low resolution inquiry image onto the low resolution image space and produce a coefficient vector, then asuper-resolution image is reconstructed via utilizing the basis vector of high-resolution image space with the obtained coefficients. This method improves the visual effect significantly; the corresponding PSNR is much largerthan other existing methods.
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