Expanding Training Data for Facial Image Super-Resolution

Abstract

The quality of training data is very important for learning-based facial image super-resolution (SR). The more similarity between training data and testing input is, the better SR results we can have. To generate a better training set of low/high resolution training facial images for a particular testing input, this paper is the first work that proposes expanding the training data for improving facial image SR. To this end, observing that facial images are highly structured, we propose three constraints, i.e., the local structure constraint, the correspondence constraint and the similarity constraint, to generate new training data, where local patches are expanded with different expansion parameters. The expanded training data can be used for both patch-based facial SR methods and global facial SR methods. Extensive testings on benchmark databases and real world images validate the effectiveness of training data expansion on improving the SR quality.