An efficient noise reduction algorithm using empirical mode decomposition and correlation measurement

Noise reduction has a lot attention no matter in practical applications or a signal processing research field. Recently, a novel denoisy method which removes noise from received signals by threshold operation on wavelet coefficients was developed and its efficiency has been confirmed. However, its definition of parameters is not general-purpose enough to deal with variant cases. In order to seek high quality to denoisy, this study introduces a frequency analysis tool, empirical mode decomposition (EMD), to separate received signal into several elements which are termed intrinsic mode functions (IMF). And then, according to the order from high frequency to low frequency, IMFs could be separated into finite pair consisting of estimated noise and estimated original signal. Each estimated pair is calculated the correlation measurement which involves second-order correlation and high-order correlation since original signal and noise are mutually independent. A smallest measure value implies an optimal pair approximating to the real. In simulations, four benchmarks and three noise level are tested; moreover, two state of the art algorithms are compared with the proposed method. Finally, the excellent robustness and efficiency of proposed method are demonstrated by simulation results.