Denoising using time-frequency and image processing methods

Douglas J. Nelson; Gabriel Cristobal; Vitaly Kober; Fehret Cakrak; Patrick J. Loughlin; Leon Cohen

doi:10.1117/12.367673

2 November 1999 Denoising using time-frequency and image processing methods

Douglas J. Nelson, Gabriel Cristobal, Vitaly Kober, Fehret Cakrak, Patrick J. Loughlin, Leon Cohen

Proceedings Volume 3807, Advanced Signal Processing Algorithms, Architectures, and Implementations IX; (1999) https://doi.org/10.1117/12.367673
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1999, Denver, CO, United States

Abstract

We present a number of methods that use image and signal processing techniques for removal of noise from a signal. The basic idea is to first construct a time-frequency density of the noisy signal. The time-frequency density, which is a function of two variables, can then be treated as an 'image,' thereby enabling use of image processing methods to remove noise and enhance the image. Having obtained an enhanced time-frequency density, one then reconstructs the signal. Various time frequency-densities are used and also a number of image processing methods are investigated. Examples of human speech and whale sounds are given. In addition, new methods are presented for estimation of signal parameters from the time- frequency density.

Citation Download Citation

Douglas J. Nelson, Gabriel Cristobal, Vitaly Kober, Fehret Cakrak, Patrick J. Loughlin, and Leon Cohen "Denoising using time-frequency and image processing methods", Proc. SPIE 3807, Advanced Signal Processing Algorithms, Architectures, and Implementations IX, (2 November 1999); https://doi.org/10.1117/12.367673

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