SF-SRGAN: PROGRESSIVE GAN-BASED FACE HALLUCINATION
Keywords: Face Hallucination, Super-resolution, Generative Adversarial Network, Progressive Upsampling, Identity Loss, Image Enhancement
Abstract. Facial hallucination is a technique that has emerged recently thanks to advances in deep learning. It can be used in various tasks such as face recognition in the wild, human identification, pedestrian re-identification, face analysis, and so on. We propose a wavelet-integrated trained face hallucination model to synthesize photorealistic face images called SF-SRGAN. The multi-stage progressive hallucination strategy is based on GAN architecture. The proposed generator consists of sequential cascade modules, each of which increases the scale by 2×. Each module has a complex structure of two branches: a progressive face hallucination branch for feature extraction and reconstruction and edge-preserving branch for high frequency detail extraction. The main difference from other progressive GAN-based face hallucination networks is that the two branches fuse followed by each cascade 2×. The model is trained and tested on popular public face datasets such as the CelebA-HQ dataset, the LFW dataset, and the Helen dataset with promising photorealistic results.