Fine-Tuning a Deconvolution Algorithm to Restore Displacement and Strain Maps Obtained with LSA

Background

Reliably measuring sharp details in displacement and strain maps returned by full-field measurement techniques remains an open question in the photomechanics community.

Objective

The primary objective of this study is to improve and fine-tune a deconvolution algorithm in order to limit the blur that obscures the details in displacement and strain maps.

Methods

Checkerboard patterns are used and processed with a spectral method, namely the Localized Spectrum Analysis (LSA), and the raw maps returned by this technique are deconvolved. The influence of various settings on the quality of the results is studied by using synthetic images deformed through a well-vetted reference displacement field.

Results

It is shown that linking the size of the analysis window used in LSA on the one hand, and the size of the second derivative kernel employed in the deconvolution algorithm on the other hand, ensures the convergence of the deconvolution algorithm in all cases. This was not the case with the initial version. The ratio between these sizes, which optimizes the metrological performance of LSA followed by deconvolution, is identified. The influence of the sampling density of the checkerboard pattern in the images is also examined. The efficiency of the deconvolution algorithm employed with optimized settings is illustrated with strain maps obtained on two specimens, one in shape memory alloy, and the other in wood.

Conclusions

It is shown in this study that deconvolution with optimized settings is an effective tool to enhance small and sharp details in strain maps obtained with LSA.