Motion Magnification

Motion magnification enhances subtle, imperceptible movements in videos, revealing information invisible to the naked eye. Current research focuses on improving the accuracy and efficiency of magnification techniques, particularly addressing challenges posed by high-speed motions, video compression, and noise, with approaches employing deep learning models (e.g., Swin Transformers), event cameras, and novel filtering strategies within Eulerian and Lagrangian frameworks. These advancements have significant implications for diverse fields, including medical diagnostics (e.g., heart rate estimation, micro-expression analysis), industrial monitoring (e.g., fault detection), and security (e.g., deepfake detection). The development of real-time, computationally efficient algorithms is a key area of ongoing investigation.