@article {12586, title = {Super-Resolution of Face Images Using Kernel PCA-Based Prior}, journal = {Multimedia, IEEE Transactions on}, volume = {9}, year = {2007}, month = {2007/06//}, pages = {888 - 892}, abstract = {We present a learning-based method to super-resolve face images using a kernel principal component analysis-based prior model. A prior probability is formulated based on the energy lying outside the span of principal components identified in a higher-dimensional feature space. This is used to regularize the reconstruction of the high-resolution image. We demonstrate with experiments that including higher-order correlations results in significant improvements}, keywords = {analysis;learning-based, analysis;probability;, component, Face, image, method;prior, model;face, principal, probability, recognition;image, reconstruction;image, reconstruction;kernel, resolution;principal, super-resolution;high-resolution}, isbn = {1520-9210}, doi = {10.1109/TMM.2007.893346}, author = {Chakrabarti,Ayan and Rajagopalan, AN and Chellapa, Rama} }