An adaptive LMS technique for wavelet polynomial threshold denoising

Sushanth Sathyanarayana; David Akopian; Sos S. Agaian

doi:10.1117/12.881297

26 May 2011 An adaptive LMS technique for wavelet polynomial threshold denoising

Sushanth Sathyanarayana, David Akopian, Sos S. Agaian

Proceedings Volume 8063, Mobile Multimedia/Image Processing, Security, and Applications 2011; 806308 (2011) https://doi.org/10.1117/12.881297
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

Abstract

Threshold operators are conventionally used in wavelet-based denoising applications. Different thresholding schemes have been suggested to achieve improved balance between mitigating various signal distortions and preserving signal details. In general, these state-of-the-art threshold operators are nonlinear shrinkage functions such as well known "soft" and "hard" thresholds and their hybrids. Recently a nonlinear polynomial threshold has been introduced which integrates several known approaches and can be optimized using a least squares technique. While significantly improving the performance - this approach is computationally intensive and is not flexible enough for band-adaptive processing. In this paper an adaptive least mean squared (LMS) optimization approach is proposed and studied which drastically reduces computational load and is convenient for band-adaptive denoising scenarios. The approach is successfully applied to 1D and 2D signals, and the results demonstrate improved performance in comparison with the conventional methods.

Citation Download Citation

Sushanth Sathyanarayana, David Akopian, and Sos S. Agaian "An adaptive LMS technique for wavelet polynomial threshold denoising", Proc. SPIE 8063, Mobile Multimedia/Image Processing, Security, and Applications 2011, 806308 (26 May 2011); https://doi.org/10.1117/12.881297

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