Translation invariant DWT based denoising using goodness of fit test | IEEE Conference Publication | IEEE Xplore

Translation invariant DWT based denoising using goodness of fit test


Abstract:

A novel signal denoising method based on discrete wavelet transform (DWT) and goodness of fit (GOF) statistical tests employing empirical distribution function (EDF) stat...Show More

Abstract:

A novel signal denoising method based on discrete wavelet transform (DWT) and goodness of fit (GOF) statistical tests employing empirical distribution function (EDF) statistics is proposed. We formulate the denoising problem into a hypothesis testing problem with a null hypothesis H0 corresponding to the presence of noise, and alternate hypothesis H representing the presence of only desired signal in the samples being tested. The decision process involves GOF tests being applied directly on multiple scales obtained from DWT. Cycle spinning approach is next employed on the de-noised data to render translation invariance property to the proposed method. We evaluate the performance of the resulting method against standard and modern wavelet shrinkage denoising methods through extensive repeated simulations performed on standard test signals.
Date of Conference: 26-29 June 2016
Date Added to IEEE Xplore: 25 August 2016
ISBN Information:
Conference Location: Palma de Mallorca, Spain

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