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This paper explores the incorporation of the two dimensional (2D) empirical mode decomposition, which is used in the Hilbert-Huang Transform, into a meaningful AM-FM image model framework. A virtue of the empirical mode decomposition is that it decomposes a non-stationary signal into stationary intrinsic mode functions and a residue signal. The empirical mode decomposition attempts to produce intrinsic mode functions that are stationary and a residue function that is dominated by piecewise monotonic functions. When considering image pixel values as produced from a non-stationary process, the 2D empirical mode decomposition shows promise as a precursor step in determining AM-FM components where stationarity is needed.