Video Processing with Eulerian Video Magnification

In our research, we aim to reveal video content that is difficult or impossible to observe with the naked eye and to represent temporal changes in an indicative manner. Our approach, termed Eulerian Video Magnification, takes standard video sequences as input and applies a spatial decomposition followed by temporal filtering across frames. The key implementation involves using pyramid decomposition (such as Laplacian or Gaussian pyramids) for spatial processing and applying temporal filters (like bandpass filters) to extract specific frequency components. The resulting signal is then amplified through multiplicative factors to reveal hidden information. Using this methodology, we can visualize flow patterns as they propagate through facial regions, while simultaneously amplifying and revealing subtle motions. Our technique can operate in real-time, displaying phenomena occurring at temporal frequencies selected by the user, with the core algorithm involving frame-by-frame processing and signal amplification through carefully tuned parameters.