Multidimensional empirical mode decomposition: Difference between revisions

Revision as of 12:37, 7 January 2022

Revision as of 01:09, 2 March 2022

Line 3:

{{context|date=May 2021}}

}}

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

==Motivation==

Multidimensional empirical mode decomposition is a popular method because of its applications in many fields, such as texture analysis, financial applications, image processing, ocean engineering, seismic research, and so onetc. Recently, several methods of Empirical Mode Decomposition have been used to analyze characterization of multidimensional signals. In this article, we will introduce the basics of Multidimensional Empirical Mode Decomposition, and then look into various approaches used for Multidimensional Empirical Mode Decomposition.

==Introduction to empirical mode decomposition (EMD)==