Equal Noise Resistance of Two Mainstream Iterative Sub-pixel Registration Algorithms in Digital Image Correlation

The inverse compositional Gauss-Newton (IC-GN) algorithm and the forward additive Newton-Raphson (FA-NR) algorithm are two mainstream iterative sub-pixel registration algorithms in digital image correlation. This study compares the accuracy and convergence ability of the two algorithms by theoretica...

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Veröffentlicht in:Experimental mechanics Jg. 57; H. 6; S. 979 - 996
Hauptverfasser: Chen, W., Jiang, Z., Tang, L., Liu, Y., Liu, Z.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.07.2017
Springer Nature B.V
Schlagworte:
Algorithms
Biomedical Engineering and Bioengineering
Characterization and Evaluation of Materials
Contamination
Control
Convergence
Correlation analysis
Digital imaging
Dynamical Systems
Engineering
Equivalence
Interpolation
Iterative methods
Lasers
Mathematical analysis
Mathematical models
Newton-Raphson method
Noise
Noise prediction
Noise reduction
Optical Devices
Optics
Photonics
Random errors
Solid Mechanics
Systematic errors
Vibration
Forward additive Newton-Raphson algorithm
Error
Gaussian noise
Digital image correlation
Inverse compositional Gauss-Newton algorithm
Convergence
ISSN:0014-4851, 1741-2765
Online-Zugang:Volltext
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Zusammenfassung:The inverse compositional Gauss-Newton (IC-GN) algorithm and the forward additive Newton-Raphson (FA-NR) algorithm are two mainstream iterative sub-pixel registration algorithms in digital image correlation. This study compares the accuracy and convergence ability of the two algorithms by theoretical analysis and numerical experiments in the speckle images that have been contaminated with artificial Gaussian noise. Based on the derived error model, the systematic errors of the two algorithms are dominated by interpolation-induced error and are insensitive to noise. The random errors are proportional to the noise level. The noise also reduces the convergence radius and rate in the two algorithms. The two algorithms demonstrate equal noise resistance due to their mathematical equivalence. These conclusions are well supported by the experimental study. The recently reported vulnerability of the FA-NR algorithm to noise is not associated with the inherent flaw of the algorithm but with its implementation. If an inappropriate method is employed to estimate the gradients at sub-pixel locations in the FA-NR algorithm, abnormally large errors may be induced. This problem can be eliminated using the method that is proposed in this study, which has an insignificant extra-computation cost.
Bibliographie:ObjectType-Article-1
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ISSN:0014-4851
1741-2765
DOI:10.1007/s11340-017-0294-y