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Statistical model for speckle pattern optimization
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DOI：10.1364/OE.25.030259.png)
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Abstract
En 中文
Image registration is the key technique of optical metrologies such as digital image correlation (DIC), particle image velocimetry (PIV), and speckle metrology. Its performance depends critically on the quality of image pattern, and thus pattern optimization attracts extensive attention. In this article, a statistical model is built to optimize speckle patterns that are composed of randomly positioned speckles. It is found that the process of speckle pattern generation is essentially a filtered Poisson process. The dependence of measurement errors (including systematic errors, random errors, and overall errors) upon speckle pattern generation parameters is characterized analytically. By minimizing the errors, formulas of the optimal speckle radius are presented. Although the primary motivation is from the field of DIC, we believed that scholars in other optical measurement communities, such as PIV and speckle metrology, will benefit from these discussions. (C) 2017 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Keywords:
DIGITAL IMAGE CORRELATION
GAUSS-NEWTON ALGORITHM
INTERPOLATION BIAS
QUALITY ASSESSMENT
SYSTEMATIC-ERRORS
NOISE
REGISTRATION
ACCURACY
DISPLACEMENT
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