Motion Blur-Based State Estimation
Motion Blur-Based State Estimation
复制标题
基于运动模糊的状态估计
DOI：
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复制
发表时间：
2016
影响因子：
4.8
通讯作者：
J. Wen
中科院分区：
文献类型：
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作者：
J. Tani;Sandipan Mishra;J. Wen
由于分辨率、响应时间、噪声水平和成本不断提高，运动测量越来越多地部署图像传感器，例如电荷耦合器件和 CMOS 阵列。典型用途是通过拍摄一系列高速清晰图片来推断运动，将图像传感器和相关光学器件用作采样器。图像模糊被视为不良伪影，需要使用较短的曝光时间或去模糊等图像处理技术来消除。我们之前已经表明，嵌入图像模糊中的动态信息可用于远远超出奈奎斯特频率的频率范围内的模型识别。在本文中，我们研究使用运动模糊的状态估计问题。我们将问题视为最小化，根据观察到的运动模糊来估计每个（慢）采样周期开始时的状态。我们证明了最小化的局部凸性对应于广义可观测性标准。该方法与其他技术进行了比较，包括传统的基于质心的方法和基于使用多个图像矩的方法。仿真和实验结果证明了该方案在存在合成杂散光的情况下的快速响应和鲁棒性。
Motion measurement increasingly deploys image sensors such as charge-coupled device and CMOS arrays, driven by their ever-improving resolution, response time, noise level, and cost. The typical usage is to operate an image sensor and the associated optics as a sampler, by taking a series of high-speed sharp pictures to infer motion. Image blur is treated as an undesirable artifact, to be removed using shorter exposure times or image processing techniques such as deblurring. We have previously shown that dynamic information embedded in image blur may be exploited for model identification in frequency ranges well beyond the Nyquist frequency. In this brief, we investigate the state estimation problem using motion blur. We pose the problem as a minimization, estimating the state at the start of each (slow) sampling period based on the observed motion blur. We show that the local convexity of the minimization corresponds to a generalized observability criterion. This method is compared with other techniques, including the conventional centroid-based method, and that based on the use of multiple image moments. The simulation and experimental results demonstrate the fast response and robustness of the proposed scheme in the presence of synthetic stray light.