Regularized Latent Space Exploration for Discriminative Face Super-Resolution

Abstract

Learning face super-resolution models is challenged in many practical scenarios where high-resolution and low-resolution face pairs usually are difficult to collect for training examples. Recent self-supervised approach provides a feasible solution by using low-resolution faces to guide the generation of the corresponding high-resolution ones with a pretrained generator. In this paper, we propose a regularized latent space exploration approach to facilitate self-supervised face super-resolution. In the approach, a pretrained generative adversarial network (GAN) is fully used to control the exploration of high-resolution face generation in an iterative optimization manner for a low-resolution face. During the iteration, super-resolution faces are continually generated from a feasible latent space by the generator and evaluated by the discriminator, while the generator is online finetuned. The generation is evaluated by measuring the semantic loss as well as pixel loss between ground-truth low-resolution faces and the corresponding downsampled super-resolution faces. In this way, the generated faces can be appearance natural and semantic discriminative. Experiments validate the effectiveness of our approach in terms of quantitative metrics and visual quality.