Pointwise intensity-based dynamic speckle analysis with binary patterns

Non-destructive detection of physical or biological activity through statistical processing of speckle patterns on the surface of diffusely reflecting objects is an area of active research. A lot of pointwise intensity-based algorithms have been proposed over the recent years. Efficiency of these algorithms is deteriorated by the signal-dependent speckle data, non-uniform illumination or varying reflectivity across the object, especially when the number of the acquired speckle patterns is limited. Pointwise processing of a sequence of 2D images is also time-consuming. In this paper, we propose to transform the acquired speckle images into binary patterns by using for a sign threshold the mean intensity value estimated at each spatial point from the temporal sequence of intensities at this point. Activity is characterized by the 2D distribution of a temporal polar correlation function estimated at a given time lag from the binary patterns. Processing of synthetic and experimental data confirmed that the algorithm provided correct activity determination with the same accuracy as the temporal normalized correlation function. It is efficient without the necessity to apply normalization at non-uniform distribution of intensity in the illuminating laser beam and offers acceleration of computation.