Space Time Video Super Resolution

Space-time video super-resolution (STVSR) aims to enhance both the spatial resolution and frame rate of videos, creating higher-quality, smoother visual experiences. Current research focuses on developing efficient and accurate deep learning models, including convolutional neural networks (CNNs), transformers, and implicit neural representations (INRs), to address challenges like motion estimation, temporal interpolation, and handling varying motion amplitudes. These advancements improve video quality for applications ranging from entertainment to scientific visualization, particularly benefiting scenarios with limited initial data quality. The field is actively exploring methods to achieve real-time performance and handle continuous, arbitrary upscaling.