  A comprehensive and systematic framework for easily extending and
implementing the subset-based spatial-temporal digital image correlation (DIC)
algorithm is presented. The framework decouples the three main factors (i.e.
shape function, correlation criterion, and optimization algorithm) involved in
algorithm implementation of DIC and represents different algorithms in a
uniform form. One can freely choose and combine the three factors to meet his
own need, or freely add more parameters to extract analytic results. Subpixel
translation and a simulated image series with different velocity characters are
analyzed using different algorithms based on the proposed framework, confirming
the merit of noise suppression and velocity compatibility. An application of
mitigating air disturbance due to heat haze using spatial-temporal DIC is given
to demonstrate the applicability of the framework.
