Component Semantic Prior Guided Generative Adversarial Network for Face Super-Resolution

Abstract

Face super-resolved (SR) images aid human perception. The state-of-the-art face SR methods leverage the spatial location of facial components as prior knowledge. However, it remains a great challenge to generate natural textures. In this paper, we propose a component semantic prior guided generative adversarial network (CSPGAN) to synthesize faces. Specifically, semantic probability maps of facial components are exploited to modulate features in the CSPGAN through affine transformation. To compensate for the overly smooth performance of the generative network, a gradient loss is proposed to recover the high-frequency details. Meanwhile, the discriminative network is designed to perform multiple tasks which predict semantic category and distinguish authenticity simultaneously. The extensive experimental results demonstrate the superiority of the CSPGAN in reconstructing photorealistic textures.

Keywords

• Facial component
• face super-resolution
• generative adversarial networks
• multiple task
• semantic prior