An improved two-step approach to hallucinating faces

Face hallucination is to synthesize a high-resolution facial image from a low-resolution input. In this paper, we present a new two-step approach to hallucinating faces motivated by the two-step algorithm of Liu et al. First, a linear relationship between both high-resolution and low-resolution facial images is established by applying PCA on both of them, and the global image, which is similar to the original high-resolution image, is reconstructed under a MAP criterion. Second, a linear model between the residual image (the difference between the original image and the global image) and the low-resolution residual image (the difference between the low-resolution input and the manually down-sampled global image) are built, and, following a MRF prior, the optimal residual image is estimated under a MAP criterion again. Experiments demonstrate that our approach can be applied to yield 4-8 fold super-resolution with high-quality hallucinated results.