Spatial-Temporal Digital Image Correlation: A Unified Framework

TL;DR

This paper introduces a flexible, unified framework for spatial-temporal digital image correlation that simplifies implementation, enhances noise suppression, and is applicable to real-world problems like heat haze mitigation.

Contribution

It presents a systematic framework decoupling key factors in DIC algorithms, enabling customizable and extendable implementations.

Findings

Effective noise suppression demonstrated

Velocity compatibility confirmed through simulations

Successful application to heat haze mitigation

Abstract

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.