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Proceedings Paper

An adaptive LMS technique for wavelet polynomial threshold denoising
Author(s): Sushanth Sathyanarayana; David Akopian; Sos S. Agaian
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Paper 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.

Paper Details

Date Published: 26 May 2011
PDF: 10 pages
Proc. SPIE 8063, Mobile Multimedia/Image Processing, Security, and Applications 2011, 806308 (26 May 2011); doi: 10.1117/12.881297
Show Author Affiliations
Sushanth Sathyanarayana, The Univ. of Texas at San Antonio (United States)
David Akopian, The Univ. of Texas at San Antonio (United States)
Sos S. Agaian, The Univ. of Texas at San Antonio (United States)

Published in SPIE Proceedings Vol. 8063:
Mobile Multimedia/Image Processing, Security, and Applications 2011
Sos S. Agaian; Sabah A. Jassim; Yingzi Du, Editor(s)

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