Bilateral Video Magnification Filter

Shoichiro Takeda, Kenta Niwa, Mariko Isogawa, Shinya Shimizu, Kazuki Okami, Yushi Aono; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 17369-17378

Eulerian video magnification (EVM) has progressed to magnify subtle motions with a target frequency even under the presence of large motions of objects. However, existing EVM methods often fail to produce desirable results in real videos due to (1) mis-extracting subtle motions with a non-target frequency and (2) collapsing results when large de/acceleration motions occur (e.g., objects suddenly start, stop, or change direction). To enhance EVM performance on real videos, this paper proposes a bilateral video magnification filter (BVMF) that offers simple yet robust temporal filtering. BVMF has two kernels; (I) one kernel performs temporal bandpass filtering via a Laplacian of Gaussian whose passband peaks at the target frequency with unity gain and (II) the other kernel excludes large motions outside the magnitude of interest by Gaussian filtering on the intensity of the input signal via the Fourier shift theorem. Thus, BVMF extracts only subtle motions with the target frequency while excluding large motions outside the magnitude of interest, regardless of motion dynamics. In addition, BVMF runs the two kernels in the temporal and intensity domains simultaneously like the bilateral filter does in the spatial and intensity domains. This simplifies implementation and, as a secondary effect, keeps the memory usage low. Experiments conducted on synthetic and real videos show that BVMF outperforms state-of-the-art methods.