In the last two decades, the interest in digital image correlation (DIC) has grown steadily in various industrial and scientific fields, thanks to its contactless nature, capability to provide full field measurements, and ease of implementation. The possibility to carry out measurements in the presence of significant relative motion between the camera and the target has been thoroughly investigated. In dynamic conditions, the main additional source of uncertainty is represented by the motion blur effect, which is experienced whenever the relative displacement between the camera and the target during the exposure time is not negligible. Motion blur is a source of uncertainty, since it decreases the contrast in the image and the definition of the speckles in the acquired pattern. In this work, a complete procedure for motion blur estimation and compensation is considered. It was found that the uncertainty in the estimation of the blur intensity and orientation plays a crucial role in motion blur compensation. In this work, we propose a technique for the optimization of the blur estimation and compensation phases based on fitting the motion optical transfer function to a motion model. Compensation with a Wiener filter is then performed, studying different approaches to image noise estimation. Results show that the optimized procedure has a positive effect on DIC performances, being able to reduce the effect of motion blur on uncertainty, in particular for motion blur levels larger than about 1–2 px. The entire work has been validated by considering the procedure performances on DIC analysis performed on different kinds of speckle image with various amounts of motion blur applied.
G et al. (Thu,) studied this question.
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