Wavelet-domain soft-thresholding for non-stationary noise

Wan Yee Lo, Ivan W. Selesnick

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper describes a new wavelet-based denoising algorithm based on a non-stationary noise assumption. Even though stationary noise models can simplify the development and implementation of denoising algorithms, they do not always accurately describe the statistical properties of the noise. The proposed algorithm has been developed to address signal processing problems under environments where the noise is spatially varying. We illustrate two signal denoising examples in order to show the performance of the proposed algorithm.

Original languageEnglish (US)
Title of host publication2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings
Pages1441-1444
Number of pages4
DOIs
StatePublished - Dec 1 2006
Event2006 IEEE International Conference on Image Processing, ICIP 2006 - Atlanta, GA, United States
Duration: Oct 8 2006Oct 11 2006

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Other

Other2006 IEEE International Conference on Image Processing, ICIP 2006
CountryUnited States
CityAtlanta, GA
Period10/8/0610/11/06

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Keywords

  • Gaussian noise
  • Image processing
  • MAP estimation
  • Poisson processes
  • Wavelet transforms

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

Cite this

Lo, W. Y., & Selesnick, I. W. (2006). Wavelet-domain soft-thresholding for non-stationary noise. In 2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings (pp. 1441-1444). [4106811] (Proceedings - International Conference on Image Processing, ICIP). https://doi.org/10.1109/ICIP.2006.312701