title: Signal-to-noise based local decorrelation compensation for speckle interferometry applications.
abstract: Speckle-based interferometric techniques allow assessing the whole-field deformation induced on a specimen due to the application of load. These high sensitivity optical techniques yield fringe images generated by subtracting speckle patterns captured while the specimen undergoes deformation. The quality of the fringes, and in turn the accuracy of the deformation measurements, strongly depends on the speckle correlation. Specimen rigid body motion leads to speckle decorrelation that, in general, cannot be effectively counteracted by applying a global translation to the involved speckle patterns. In this paper, we propose a recorrelation procedure based on the application of locally evaluated translations. The proposed procedure implies dividing the field into several regions, applying a local translation, and calculating, in every region, the signal-to-noise ratio (SNR). Since the latter is a correlation indicator (the noise increases with the decorrelation) we argue that the proper translation is that which maximizes the locally evaluated SNR. The search of the proper local translations is, of course, an interactive process that can be facilitated by using a SNR optimization algorithm. The performance of the proposed recorrelation procedure was tested on two examples. First, the SNR optimization algorithm was applied to fringe images obtained by subtracting simulated speckle patterns. Next, it was applied to fringe images obtained by using a shearography optical setup from a specimen subjected to mechanical deformation. Our results show that the proposed SNR optimization method can significantly improve the reliability of measurements performed by using speckle-based techniques.
authors.affiliation: LTDS, UMR CNRS/ECL/ENISE/ENSMSE 5513, Ecole des Mines de Saint-Etienne, Saint-Etienne, France. molimard@emse.fr
authors.forename: Jérôme
authors.initials: J
authors.lastname: Molimard
authors.forename: Raul
authors.initials: R
authors.lastname: Cordero
authors.forename: Alain
authors.initials: A
authors.lastname: Vautrin