18th International Conference on Pattern Recognition, Hong Kong
A Regression Model in Tensor PCA Subspace for Face Image Super-Resolution Reconstruction
作者:
Wu， J. and Trivedi， M.M.
关键词:
image reconstruction；image resolution；maximum likelihood estimation；principal component analysis；probability；radial basis function networks；regression analysis；tensors；FERET database；RBF-type regressor
摘要:
A regression model in the tensorPCA subspace is proposed in this paper for face super-resolution reconstruction. An approximate conditional probability model is used for the tensor subspace coefficients and maximum-likelihood estimator gives a linear regression model. The approximation is corrected by adding non-linear component from a RBF-type regressor. Experiments on face images from FERET database validate the algorithm. Although each projection coefficient is estimated by a local estimator, tensorPCA subspace analysis is still a global descriptor, which makes the algorithm have certain ability to deal with partially occluded images.
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