Face Super Resolution

Face super-resolution (FSR) aims to reconstruct high-resolution facial images from low-resolution inputs, improving image quality for various applications. Current research heavily utilizes deep learning, focusing on architectures like generative adversarial networks (GANs), diffusion models, and CNN-Transformer hybrids, often incorporating techniques such as attention mechanisms, multi-scale feature fusion, and knowledge distillation to enhance performance and address challenges like handling diverse low-resolution inputs and maintaining identity consistency. These advancements are significant for improving facial recognition accuracy, enhancing video quality, and enabling more robust applications in surveillance, healthcare (e.g., remote photoplethysmography), and other fields relying on facial image analysis.