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Digital image correlation with reduced bias error based on digital signal upsampling theory

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Abstract

Based on digital signal upsampling theory, a new computing strategy has been proposed to reduce the bias error in digital image correlation (DIC) caused by intensity interpolation. For each subset, before subpixel image matching, the subimage around the target subset was processed by increasing the sampling rate with an integer factor. The increase of the sampling rate is realized by resampling in the digital domain. The combination of digital signal upsampling processing with DIC can greatly reduce the interpolation bias error. The measurement accuracy of the proposed computing strategy was investigated in this study. Both numerical experiments and real-world experiments have been conducted in order to verify the effectiveness of the proposed computing strategy. The results indicate that the bias error can be significantly reduced without sacrificing the standard deviation error. With the proposed computing strategy, high-accuracy DIC measurement with near-negligible bias error is expected.

© 2019 Optical Society of America

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