Face hallucination using example-based regularization

Face super-resolution is to synthesize a high resolution facial image from a low resolution input, which can significantly improve the recognition for computer and human. Regularization plays a vital role in ill-posed problems. The use of examples becomes much more effective when handling narrow family of images, such as face images. A properly chosen regularization can direct the solution toward a better quality outcome. An emerging powerful regularization is one that leans on image examples. This paper proposed a face hallucination method using example-based regularization. The work is specially targeted at improving the quality of high magnification. Our work follows the pyramid framework and assigns several high-quality candidate patches for each location in the degraded image. All problematic examples are rejected by defining an error function which embodies the example-based regularization. After repeated pruning, the reconstruction is done when there is only one candidate patch left in each location. The encouraging experimental results provide some hints that our approach is effective.