Modified noncausal smoothing filter and low rank matrix approximation for noise reduction

Teeradache Viangteeravat; D. Mitchell Wilkes

doi:10.1117/12.858034

27 April 2010 Modified noncausal smoothing filter and low rank matrix approximation for noise reduction

Teeradache Viangteeravat, D. Mitchell Wilkes

Proceedings Volume 7697, Signal Processing, Sensor Fusion, and Target Recognition XIX; 76971K (2010) https://doi.org/10.1117/12.858034
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

Removing noise in real time has become a high priority for analyzing data corrupted by additive noise. It is a major problem in various applications such as speech, image processing and real time multimedia services. Although considerable interest has arisen in recent years regarding wavelets as a new transform technique for many applications, the linear adaptive decomposition transform (LDT) has yielded results superior to the discrete wavelet transform (DWT) not only in terms of using a lower number of decomposition levels but also achieving a smaller percentage normalized approximation error in the reconstructed signal. In this paper, a novel noise reduction method, based on a modified noncausal smoothing filter and low rank approximation based upon the sum of minimum magnitude error criterion (i.e., l₁ norm) is introduced that distinguishes itself from these other methods. The performance of the proposed approach was evaluated based on one dimensional data sets as well as speech samples. It is demonstrated that the approach yields very promising results on the test signals of the Donoho and Johnstone as well as to speech signals.

Citation Download Citation

Teeradache Viangteeravat and D. Mitchell Wilkes "Modified noncausal smoothing filter and low rank matrix approximation for noise reduction", Proc. SPIE 7697, Signal Processing, Sensor Fusion, and Target Recognition XIX, 76971K (27 April 2010); https://doi.org/10.1117/12.858034

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