Capsule GAN for robust face super resolution

Abstract

Face hallucination is an emerging sub-field of Super-Resolution (SR) which aims to reconstruct the High-Resolution (HR) facial image given its Low-Resolution (LR) counterpart. The task becomes more challenging when the LR image is extremely small due to the image distortion in the super-resolved results. A variety of deep learning-based approaches has been introduced to address this issue by using attribute domain information. However, a more complex dataset or even further networks is required for training these models. In order to avoid these complexities and yet preserve the precision in reconstructed output, a robust Multi-Scale Gradient capsule GAN for face SR is proposed in this paper. A novel similarity metric called Feature SIMilarity (FSIM) is introduced as well. The proposed network surpassed state-of-the-art face SR systems in all metrics and demonstrates more robust performance while facing image transformations.