  Iterative Filtering (IF) is an alternative technique to the Empirical Mode
Decomposition (EMD) algorithm for the decomposition of non-stationary and
non-linear signals. Recently in [1] IF has been proved to be convergent for any
$L^2$ signal and its stability has been also showed through examples.
Furthermore in [1] the so called Fokker-Planck (FP) filters have been
introduced. They are smooth at every point and have compact supports. Based on
those results, in this paper we introduce the Multidimensional Iterative
Filtering (MIF) technique for the decomposition and time-frequency analysis of
non-stationary high-dimensional signals. And we present the extension of FP
filters to higher dimensions. We illustrate the promising performance of MIF
algorithm, equipped with high-dimensional FP filters, when applied to the
decomposition of 2D signals.
  [1] A. Cicone, J. Liu, and H. Zhou, Adaptive local iterative filtering for
signal decomposition and instantaneous frequency analysis, arXiv:1411.6051,
2014.
