Video Super Resolution

Video super-resolution (VSR) aims to enhance the resolution of low-resolution videos, improving visual quality for applications like streaming and broadcasting. Current research emphasizes developing efficient algorithms, often employing recurrent neural networks, transformers, and generative adversarial networks (GANs), to achieve real-time performance and high fidelity, particularly on resource-constrained devices. This field is significant due to its potential to improve the viewing experience of low-quality video content across various platforms and devices, and ongoing work focuses on addressing challenges like temporal consistency, artifact reduction, and efficient model design.