A new rapid auto-adapting diffusion function for adaptive anisotropic image de-noising and sharply conserved edges

Anisotropic diffusion, based on partial differential equation (PDE), is a recent adequate solution for the problem of image filtering. The first works in this context are those of Perona and Malik. Recently, several studies have shown the drawbacks of this approach such as the ”staircase” effect and flow edges caused by the slow convergence of the diffusion function. In this work, we suggest a new diffusion function, which converges faster than that of Perona and Malik. The suggested function decreases rapidly to disappear once borders or details are detected. This rapidity to expand and converge to zero allows us to implement a real time processing device. Moreover, the suggested model is able to remove the ”staircase” effect, preserve sharp transition and discontinuities and remove noise efficiently. The diffusion barrier is chosen to get rid of the noise and enhance the edges. Extensive experiments on several standard test images are conducted to compare our algorithm with other well-known algorithms. Experimental results are very interesting and show the efficiency of the suggested method based on a comparison study.