Image processing is realized by using the FitzHugh Nagumo model which is a Reaction-Diffusion equation. When
the system is bistable and choosing a set of proper parameters, contrast enhancement and image smoothing can be
obtained. The processed effects are satisfying at the earlier phase of image processing.
Image smoothing is realized by using a FHN (FitzHugh Nagumo) model which is a typical partial differential equation. The model exhibits three characteristics of dynamics: excitable, Turing/Hopf instability, and bistable. In the bistable region diffusion process in space leads to the availability of image smoothing and decides the smoothing effects. After comparing with average filter and median filter it is found that the effects of image smoothing by FHN model are as good as that by other filters. Results show that this approach is effective to image smoothing.
Edge detection is realized by using a FitzHugh Nagumo model which is one type of partial differential equations.
This model has three types of dynamic, excitable, Turing/Hopf bifurcation, and bistable. In the excitable region the
model can realize the edge detection. In the simulation only one image is processed in order to confirm the effect of
control parameters on the edge detection. A satisfying effect of edge detection can be obtained by choosing appropriate
control parameters. By comparing with other operators it is found that the FitzHugh Nagumo model is superior to Canny
operator, Prewitt operator, Roberts operator, and Sobel operator on edge detection.