Speckle Patterns and 2-Dimensional Spatial Models

TL;DR

This paper models speckle intensity patterns using 2D Gaussian lattice fits and Monte Carlo simulations, revealing how nearest-neighbor interactions influence spatial variance and speckle size.

Contribution

It introduces a combined approach of Gaussian lattice fitting and Monte Carlo simulation to analyze speckle patterns with nearest-neighbor interactions.

Findings

Nearest-neighbor interactions create a stationary stochastic process.

Changing mesh size affects speckle size and intensity averaging.

Simulation results match theoretical spatial variance structures.

Abstract

The result of 2-dimensional Gaussian lattice fit to a speckle intensity pattern based on a linear model that includes nearest-neighbor interactions is presented. We also include a Monte Carlo simulation of the same spatial speckle pattern that takes the nearest-neighbor interactions into account. These nearest-neighbor interactions lead to a spatial variance structure on the lattice. The resulting spatial pattern fluctuates in value from point to point in a manner characteristic of a stationary stochastic process. The value at a lattice point in the simulation is interpreted as an inten-sity level and the difference in values in neighboring cells produces a fluctuating intensity pattern on the lattice. Changing the size of the mesh changes the relative size of the speckles. Increasing the mesh size tends to average out the intensity in the direction of the mean of the stationary process.