What is Multidimensional empirical mode decomposition?

In signal processing, multidimensional empirical mode decomposition is an extension of the one-dimensional EMD algorithm to a signal encompassing multiple dimensions. The Hilbert–Huang empirical mode decomposition process decomposes a signal into intrinsic mode functions combined with the Hilbert spectral analysis, known as the Hilbert–Huang transform. The multidimensional EMD extends the 1-D EMD algorithm into multiple-dimensional signals. This decomposition can be applied to image processing, audio signal processing, and various other multidimensional signals.