Wavelet-based adaptive image denoising with edge preservation

Charles Q. Zhan, Lina Karam

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

20 Scopus citations


This paper presents a state-of-the-art adaptive wavelet-based denoising method with edge preservation. More specifically, a redundant discrete dyadic wavelet transform (DDWT) is performed on the noisy image to get the wavelet frame decomposition at different scales. Based on the Lipschitz regularity theory, correlation analysis across scales is performed to detect the significant coefficients from the signal and the insignificant coefficients from the noise for each subband. Different denoising techniques are applied to the significant coefficients and insignificant coefficients separately, based on different statistical models. Unlike most of the existing image denoising methods, the proposed method is able to not only shrink but also increase the magnitude of the noisy wavelet coefficients. Simulation results show that the proposed method has a remarkably superior ability to preserve the edge information and to achieve better visual quality.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Image Processing
Number of pages4
StatePublished - 2003
EventProceedings: 2003 International Conference on Image Processing, ICIP-2003 - Barcelona, Spain
Duration: Sep 14 2003Sep 17 2003


OtherProceedings: 2003 International Conference on Image Processing, ICIP-2003

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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