Hallucinating Face by Eigentransformation with Distortion Reduction

In this paper, we propose a face hallucination method using eigentransformation with distortion reduction. Different from most of the proposed methods based on probabilistic models, this method views hallucination as a transformation between different image styles. We use Principal Component Analysis (PCA) to fit the input face image as a linear combination of the lowresolution face images in the training set. The high-resolution image is rendered by replacing the low-resolution training images with the high-resolution ones, while keeping the combination coefficients. Finally, the nonface-like distortion in the hallucination process is reduced by adding constraints to the principal components of the hallucinated face. Experiments show that this method can produce satisfactory result even based on a small training set.