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.

中文翻译：

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在视频测量中可视化和量化小的和非平稳的结构运动

视觉记录技术的快速进步正在增加视频数据在广泛应用中的重要性和产量。本文提出了一种多频相位推断的新观点，用于表征光学测量中特别具有挑战性的非平稳且通常很小的运动。该模型通过多频相位检索估计和调整相位信息，多频相位检索源自具有块匹配 3D 稀疏先验的最大似然公式。通过 2D 相位展开问题的稳健解决方案消除了估计的相位跳跃。这些考虑得到了结构健康监测中动态响应识别应用的支持。与最先进的技术相比，所提出的方法很容易在真实视频上产生高质量的放大倍率，噪音更小，抗噪性能更好。所提出的方法还展示了在提取更清晰的视频分量时域运动估计方面的高技能。