Abstract
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 a super-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 larger than other existing methods.
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References
Netravali, N., Haskell, B.G.: Digital Pictures: Representation, Compression and Standards, 2nd edn. Plenum Press, New York (1995)
Schultz, R., Stevenson, R.L.: A Bayesian Approach to Image Expansion for Improved Definition. IEEE Trans. Image Processing 3(3), 233–242 (1994)
Freeman, W.T., Paztor, E.C.: Example-based Super-resolution. IEEE Computer Graphics and Applications, 55–65 (2002)
Frank, M., Candocia, Jose, C.: Principe: Super- Resolution of Images Based on Local Correlation. IEEE Transactions on Neural Networks 10(2), 372–380 (1999)
Baker, S., Kanade, T.: Limits on Super-Resolution and How to Break them. IEEE Trans. on PAMI 24(9), 1167–1183 (2002)
Baker, S., Kanade, T.: Hallucinating Faces. In: Proc. of IEEE Inter. Conf. on Automatic Face and Gesture Recognition, pp. 83–88 (2003)
Liu, W., Lin, D.H., Tang, X.O.: Hallucinating faces: Tensor patch super-resolution and coupled residue compensation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 478–484 (2005)
Zhuang, Y.T., Zhang, J., Wu, F.: Hallucinating faces: LPH super-resolution and neighbor reconstruction for residue compensation. Pattern Recognition 40, 3178–3194 (2007)
Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: A literature survey. ACM Computing Surveys 35(4), 399–458 (2003)
Zhao, W., Chellapa, W., Philips, P.: Subspace linear discriminate analysis for face recognition. Technical report. CAR-TR-914 (1996)
Wang, X.G., Tang, X.O.: Hallucinating face by eigen transformation. IEEE Trans. Syst. Man Cybern. 35(3), 425–434 (2005)
Gonzalez, R.C., Woods, R.G.: Digital Image Processing, 2nd edn. Prentice-Hall, Englewood Cliffs (2002)
Park, S.C., Park, M.K., Kang, M.G.: Super-resolution image reconstruction: a technical overview. IEEE Signal Processing Magazine (2003)
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Yang, D., Xu, T., Yang, R., Liu, W. (2009). Face Image Enhancement via Principal Component Analysis. In: Nicholson, A., Li, X. (eds) AI 2009: Advances in Artificial Intelligence. AI 2009. Lecture Notes in Computer Science(), vol 5866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10439-8_20
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DOI: https://doi.org/10.1007/978-3-642-10439-8_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10438-1
Online ISBN: 978-3-642-10439-8
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