Extreme face super-resolution (FSR), that is, improving the resolution of face images by an extreme scaling factor (often greater than ×8) has remained underexplored in the literature of low-level vision. Extreme FSR in the wild must address the challenges of both unpaired training data and unknown degradation factors. Inspired by the latest advances in image super-resolution (SR) and self-supervised learning (SSL), we propose a novel two-step approach to FSR by introducing a mid-resolution (MR) image as the stepping stone. In the first step, we leverage ideas from SSL-based SR reconstruction of medical images (e.g., MRI and ultrasound) to modeling the realistic degradation process of face images in the real world; in the second step, we extract the latent codes from MR images and interpolate them in a self-supervised manner to facilitate artifact-suppressed image reconstruction. Our two-step extreme FSR can be interpreted as the combination of existing self-supervised CycleGAN (step 1) and StyleGAN (step 2) that overcomes the barrier of critical resolution in face recognition. Extensive experimental results have shown that our two-step approach can significantly outperform existing state-of-the-art FSR techniques, including FSRGAN, Bulat's method, and PULSE, especially for large scaling factors such as 64.
Face image synthesis has advanced rapidly in recent years. However, similar success has not been witnessed in related areas such as face single image super-resolution (SISR). The performance of SISR on real-world low-quality face images remains unsatisfactory. In this paper, we demonstrate how to advance the state-of-the-art in face SISR by leveraging style-based generator in unsupervised settings. For real-world low-resolution (LR) face images, we propose a novel unsupervised learning approach by combining style-based generator with relativistic discriminator. With a carefully designed training strategy, we demonstrate our converges faster and better suppresses artifacts than Bulat’s approach. When trained on an ensemble of high-quality datasets (CelebA, AFLW, LS3D-W, and VGGFace2), we report significant visual quality improvements over other competing methods especially for real-world low-quality face images such as those in Widerface. Additionally, we have verified that both our unsupervised approaches are capable of improving the matching performance of widely used face recognition systems such as OpenFace.
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