Subband adaptive image denoising via bivariate shrinkage

Levent Şendur, Ivan W. Selesnick

Research output: Contribution to conferencePaper

Abstract

It is well known that the wavelet coefficients of natural images have significant statistical dependencies. To model the non-Gaussian nature of these statistics, a new bivariate pdf is proposed in this paper and applied to the image denoising problem. For this purpose, the corresponding new bivariate shrinkage function is derived using MAP estimator. Using this function, a subband dependent data-driven system is described and applied to both orthogonal and dual-tree complex wavelet coefficients. Also, some comparisons to the other effective data-driven techniques are given.

Original languageEnglish (US)
PagesIII/577-III/580
StatePublished - 2002
EventInternational Conference on Image Processing (ICIP'02) - Rochester, NY, United States
Duration: Sep 22 2002Sep 25 2002

Other

OtherInternational Conference on Image Processing (ICIP'02)
CountryUnited States
CityRochester, NY
Period9/22/029/25/02

ASJC Scopus subject areas

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

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    Şendur, L., & Selesnick, I. W. (2002). Subband adaptive image denoising via bivariate shrinkage. III/577-III/580. Paper presented at International Conference on Image Processing (ICIP'02), Rochester, NY, United States.