Temporal Interpolation

Temporal interpolation aims to generate intermediate data points between existing observations in time-series data, improving resolution and filling gaps. Current research focuses on developing deep learning-based methods, including convolutional and recurrent neural networks, often incorporating techniques like cycle consistency and feature interpolation to enhance accuracy and efficiency across diverse data types such as videos, medical images, and geospatial data. These advancements are significant for various applications, improving the quality of scientific visualizations, enabling more efficient medical imaging, and enhancing video processing and editing capabilities. The field is also exploring ways to integrate physical constraints and prior knowledge into interpolation models to improve accuracy and generalization.