Using chaotic dynamics to characterize the complexity of rough surfaces

In this work, we use the basic ingredients of chaotic dynamics (stretching and folding of phase space points) for the characterization of the complexity of microscopy images of rough surfaces. The key idea is to use an image as the initial condition of a chaotic discrete dynamical system, such as the Arnold cat map, and track its transformations during the first iterations of the map. Since the basic effects of the Arnold map are the stretching and folding of image texture, the application of the map leads to an enhancement of the high frequency content of images along with an increase of discontinuities in pixel intensities. We exploit these effects to quantify the complexity of S type (lying between homogeneity and randomness) of the image texture since the first (enhancement of high frequencies) can be used to quantify the distance of texture from randomness and noise and the second (the proliferation of discontinuities) the distance from order and homogeneity. The method is validated in synthetic images which are generated from computer generated surfaces with controlled correlation length and fractal dimension and it is applied in real images of nanostructured surfaces obtained from a scanning electron microscope with very interesting results.

Automated summary

In this work, the complexity of microscopy images of rough surfaces is characterized using the basic ingredients of chaotic dynamics. An image is used as the initial condition of a chaotic discrete dynamical system, and its transformations during the first iterations of the map are tracked. The stretching and folding of image texture caused by the map are exploited to quantify the complexity of the image texture. The method is validated in synthetic and real images, showing interesting results.