Fractal Residual Network for Face Image Super-Resolution

Abstract

Recently, many Convolutional Neural Network (CNN) algorithms have been proposed for image super-resolution, but most of them aim at architecture or natural scene images. In this paper, we propose a new fractal residual network model for face image super-resolution, which is very useful in the domain of surveillance and security. The architecture of the proposed model is composed of multi-branches. Each branch is incrementally cascaded with multiple self-similar residual blocks, which makes the branch appears as a fractal structure. Such a structure makes it possible to learn both global residual and local residual sufficiently. We propose a multi-scale progressive training strategy to enlarge the image size and make the training feasible. We propose to combine the loss of face attributes and face structure to refine the super-resolution results. Meanwhile, adversarial training is introduced to generate details. The results of our proposed model outperform other benchmark methods in qualitative and quantitative analysis.