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

Area of Science:

• Mechanical Engineering
• Signal Processing
• Image Analysis

Background:

• Digital Image Correlation (DIC) is a widely used optical measurement technique.
• Intensity interpolation in DIC can introduce bias errors, affecting measurement accuracy.
• Reducing interpolation bias is crucial for enhancing DIC precision.

Purpose of the Study:

• To propose and validate a novel computing strategy for Digital Image Correlation (DIC).
• To significantly reduce bias error in DIC measurements caused by intensity interpolation.
• To maintain or improve measurement accuracy in DIC.

Main Methods:

• The proposed method is based on digital signal upsampling theory.
• Subimages are resampled in the digital domain to increase the sampling rate before subpixel matching.
• The strategy combines digital signal upsampling with DIC processes.

Main Results:

• Numerical and real-world experiments validated the effectiveness of the proposed strategy.
• The method significantly reduced bias error in DIC measurements.
• The standard deviation error was not compromised, indicating maintained precision.

Conclusions:

• The novel computing strategy effectively minimizes interpolation bias error in DIC.
• This approach enables high-accuracy DIC measurements with near-negligible bias error.
• The method offers a significant advancement for precise digital image correlation applications.