Wavelet-Based Video Motion Magnification for Enhanced Visual Perception

Abstract

Existing video motion magnification methods commonly decompose video frames into multiple spatial frequency bands using pyramid representations, such as the phase-based magnification method and the fast Riesz-pyramid approach. These pyramid-based decompositions, in combination with Gabor or Hilbert transforms, extract the instantaneous phase of images across different orientations. Motion magnification is then achieved by amplifying the phase variations between consecutive frames, followed by image reconstruction. However, due to the inherent limitations of the Gabor and Hilbert transforms, these approaches can only effectively capture instantaneous phase information in low- and mid-frequency ranges, while high-frequency components are not synchronously magnified. This mismatch often results in reconstruction artifacts. To overcome this limitation, this paper proposes a wavelet-based video motion magnification framework. The proposed method employs a spatial Butterworth filter to decompose video frames into low-, mid-, and high-frequency bands, and leverages the wavelet transform to extract high-frequency phase information for synchronous magnification. By enhancing the representation of high-frequency components, the method effectively suppresses artifacts and improves visual fidelity. Experimental results demonstrate that the proposed approach achieves artifact-free motion magnification with superior accuracy and visual quality compared to existing methods.