A 2D model for face superresolution

Traditional face superresolution methods treat face images as 1D vectors and apply PCA on the set of these 1D vectors to learn the face subspace. Zhang et al [7] proposed Two-directional two-dimensional PCA (2D)²-PCA for efficient face representation and recognition where images are treated as matrices instead of vectors. In this paper, we present a two-step algorithm for face superresolution. In first step, we propose a 2D-framework for face superresolution where the face image is treated as a matrix. (2D)²-PCA is used for learning face subspace and a MAP estimator is used to obtain the global high resolution image from the given low resolution image. To enhance the quality of the image further, we propose a method which uses Kernel Ridge Regression to learn the high frequency component relation between low and high resolution patches of the image. Experimental results show that our approach can reconstruct high quality face images.