A Wavelet-based Learning for Face Hallucination with Loop Architecture

Cong Geng, Li Chen, Xiaoyun Zhang, Peng Zhou, Zhiyong Gao

Abstract: Face hallucination is a specific super-resolution problem which aims to generate high-resolution(HR) faces from low-resolution(LR) input. Recently, deep learning methods have been widely applied in single-image super resolution. Considering face images have great similarities in both pixel value and global structure, we propose a wavelet-based deep learning method with loop architecture for face hallucination. In contrast to existing wavelet-based methods that generate wavelet coefficients independently without considering relationships between them, we propose a three-stage method with loop architecture. This alternately updated loop structure explores the statistical relationships among wavelet coefficients and has a maximum use of information flow with a small number of parameters. Because of multi-resolution property of wavelet transform, we adopt a mixed input strategy to train images with different sizes to realize multi-scale face hallucination without retraining and adding extra sub-networks. Experiments demonstrate that our method can get a robust performance with multi-scale face hallucination.