Cornell University

Madhur Srivastava

Jack H Freed

C Lindsay Anderson

Lindsay C Anderson

Methods, systems and programs for denoising a signal using an undecimated discrete wavelet transformation are provided. The methods use signal location windows. The signal location windows may be used before or after thresholding. When signal location windows are used after thresholding coefficients within the signal location windows may be restored to their original values. When signal location windows are used before thresholding, coefficients within the signal location windows may be unchanged by the thresholding. The signal location windows may be used only on a subset of decomposition levels that are thresholded. The signal location windows may be used on both the Detail components and the highest decomposition level for thresholding of the Approximation component.

Wavelet denoising using signal location windowing

Methods, systems and programs for denoising a signal using discrete wavelet transformation are provided. For example, a method for denoising a signal may include determining a number of resolution levels to denoise, determining variable threshold(s) for each resolution level, applying the determined variable threshold(s) to denoise at least a detail component of each of the determined resolution levels. Each variable threshold includes a separately determined lower threshold and upper threshold. The method for denoising a signal may further include transforming, using an inverse discrete wavelet transformation, at least the denoised detail component for each of the determined resolution levels into a denoised signal.

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Ithaca, NY, United States of America

Madhur Srivastava