Low Resolution Face

Low-resolution face image processing focuses on enhancing the quality and information content of faces captured at low resolution, aiming to improve applications like face recognition and video restoration. Current research emphasizes developing sophisticated deep learning models, including generative adversarial networks (GANs), diffusion models, and transformers, often incorporating techniques like knowledge distillation and wavelet transforms to improve efficiency and accuracy. These advancements are crucial for improving the performance of various computer vision systems, particularly in scenarios with limited image quality, and contribute to a broader understanding of image restoration and super-resolution techniques. The development of new benchmark datasets, focusing on real-world video and diverse degradations, is also driving progress in this field.