Scatterer recognition via analysis of speckle patterns

We have demonstrated that even though speckle patterns from different objects are very similar, they contain minute dissimilarities that can be used to differentiate between the originating scatterers. We first approached this problem using a Convolutional Neural Network - a deep learning algorithm, to show that indeed specific speckle patterns can be linked to the respective materials creating them.

We then progressed to use recorded speckle patterns created from different materials in order to measure statistical parameters that possess a well-defined physical meaning. Using these parameters gave similar scatterer recognition abilities while gaining insight on the physical reasons for these material dependent statistical deviations.