Strain field denoising for digital image correlation using a regularized cost-function

• Published in: Optics and lasers in engineering Vol. 65; pp. 9 - 17
• Main Authors: Pan, Bing, Yuan, Jieyu, Xia, Yong
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-02-2015
• Subjects: Algorithms; Correlation; Denoising; Digital; Digital image correlation; Displacement/strain; Equivalence; Least squares method; Noise reduction; Strain; Digital image correlation; Displacement/strain; Denoising
• ISSN: 0143-8166; 1873-0302
• DOI: 10.1016/j.optlaseng.2014.03.016

In digital image correlation (DIC), the widely used forward-additive Newton–Raphson (FA-NR) algorithm and the recently introduced equivalent but more efficient inverse-compositional Gauss–Newton (IC-GN) algorithm are capable of providing both displacements and displacement gradients (strains) for each calculation point. However, the obtained displacement gradients are seriously corrupted by various noises, and for this reason these directly computed strains are usually considered as useless information and therefore discarded. To extract strain distributions more accurately, much research efforts have been dedicated to how to smooth and differentiate the noisy displacement fields using appropriate numerical approaches. In this contribution, contrary to these existing strain estimation approaches, a novel and alternative strain estimation approach, based on denoising the noisy strain fields obtained by FA-NR or IC-GN algorithm using a regularized cost-function, is proposed. The effectiveness and practicality of the proposed strain estimation technique is carefully examined using both computer-simulated images with imposed homogeneous and inhomogeneous deformation, and experimentally obtained images. Experimental results reveal that the strains obtained by the proposed method are comparable to those determined by post-processing of the displacement fields using conventional pointwise least squares strain estimation approach.
•Strains directly offered by NR algorithm are normally very noisy and unreliable.•A novel strain denoising technique using a regularized cost-function is proposed.•The denoised strains are comparable to those obtained by existing strain estimation approach.•Numerical and real experiments verified the effectiveness of the proposed technique.