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In this compressed computation, we have used the approximate dyadic filter bank properties of EMD/EEMD.
Note that a detailed knowledge of the intrinsic mode functions of a noise corrupted signal can help in estimating the significance of that mode. It is usually assumed that the first IMF captures most of the noise and hence from this IMF we could estimate the Noise level and estimate the noise corrupted signal eliminating the effects of noise approximately. This method is known as denoising and detrending. Another advantage of using the MEEMD is that the mode mixing is reduced significantly due to the function of the EEMD.<br />The denoising and detrending strategy can be used for image processing to enhance an image and similarly it could be applied to Audio Signals to remove corrupted data in speech. The MDEEMD could be used to break down images and audio signals into IMF and based on the knowledge of the IMF perform necessary operations. The decomposition of an image is very advantageous for
== Parallel implementation of multi-dimensional ensemble empirical mode decomposition.<ref name=":8" /> ==
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