Visualizing and quantifying small and nonstationary structural motions in video measurement

Rapid advancement in vision recording technologies is increasing the importance and production of video data in a wide range of applications. This paper proposed a novel perspective of multifrequency phase inference for characterizing especially challenging nonstationary and often small motions in optical measurement. The model estimates and adjusts the phase information by the multi-frequency phase retrieval, which is derived from the maximum likelihood formulation with block matching 3D sparsity priors. Estimated phase jumps are removed by a robust solution of the 2D phase unwrapping problem. These considerations are supported by applications of dynamic response identification in structural health monitoring. When compared to state-of-the-art techniques, the proposed method readily yielded high-quality magnifications on real videos, with less noise and better anti-noise performance. The proposed method also demonstrated uniformly high skill in extracting clearer time-domain motion estimation of video components.