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Anisotropic diffusion filtering for signal smoothing as a low-pass filter hasthe advantage of the edge-preserving, i.e., it does not affect the edges thatcontain more critical data than the other parts of the signal. In this paper,we present a numerical algorithm based on least squares support vectorregression by using Legendre orthogonal kernel with the discretization of thenonlinear diffusion problem in time by the Crank-Nicolson method. This methodtransforms the signal smoothing process into solving an optimization problemthat can be solved by efficient numerical algorithms. In the final analysis, wehave reported some numerical experiments to show the effectiveness of theproposed machine learning based approach for signal smoothing.

A least squares support vector regression for anisotropic diffusion filtering