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. Author manuscript; available in PMC: 2017 Jul 7.
Published in final edited form as: IEEE Access. 2016 Jul 7;4:3862–3877. doi: 10.1109/ACCESS.2016.2587581

Algorithm 1.

Wavelet Shrinkage Denoising

  1. Select a wavelet.

  2. Select k (1 ≤ k ≤ M) decomposition levels to denoise the Detail components, where M = ⌊log2(N)⌋, and N = length(X), and X is the discrete input signal.

  3. Take the kth level discrete wavelet transform (DWT) of the discrete input signal X, decomposing it into k decomposition levels, also referred to as k Detail components and the kth Approximation component.

  4. Estimate the noise for the k Detail components.

  5. Calculate the noise threshold for the k Detail components.

  6. Apply noise thresholding to the k selected Detail components.

  7. Take the inverse discrete wavelet transform (IDWT) of the resultant k Detail components and the kth Approximation component.