Evolutionary noise reduction based on correlation evaluation

This paper addresses on recovering signals from white noise degradation without prior knowledge about pure signals. For the issue, wavelet-based methods support some acceptable solution, but their adaptability to various signal types is not good enough to perform more excellent. In order to deal the problem, this paper proposes an evolutionary strategy to choose a better split point of wavelet shrinkage. Moreover, the correlation evaluation of signals applied on evolutionary optimization makes the proposed method to remove noise more efficient. The advantage is demonstrated by simulation experiment having some benchmark cases and various noise degradations.