Normalization in dynamic speckle analysis for non-destructive monitoring of speed of processes

Abstract The paper is dedicated to analysis of normalized intensity-based pointwise algorithms for processing dynamic speckle images with spatially varying speckle statistics in non-destructive visualization of regions of faster or slower changes across an object. Both existing and newly proposed algorithms are analyzed. Extraction of speed of changes is done by acquiring correlated in time speckle images formed on the object surface under laser illumination. The studied algorithms have been applied to simulated low and high contrast speckle data. Their performance has been compared to processing of binary patterns as another approach for dealing with varying speckle statistics in the acquired images. The efficiency of the algorithms have been checked on the experimental data, including data in a compressed format. We have proven that the algorithms with normalization at successive instants by a sum of two intensities or a single intensity outperform as a whole the algorithms which apply the time-averaged estimates of the mean value and the variance of speckle intensity.