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.
A learning-based method to super-resolve face images using a kernel PCA-based prior model is presented in REF , which focuses on the specific application of face recognition.
Super-Resolution of Face Images Using Kernel PCA-Based Prior
IEEE Transactions on Multimedia
IEEE Transactions on Multimedia
Computer Science