Abstract
AbstractRapid 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.
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