Subband adaptive image denoising via bivariate shrinkage

Levent Şendur; Ivan W. Selesnick

Subband adaptive image denoising via bivariate shrinkage

Electrical and Computer Engineering

Research output: Contribution to conference › Paper › peer-review

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 language	English (US)
Pages	III/577-III/580
State	Published - 2002
Event	International Conference on Image Processing (ICIP'02) - Rochester, NY, United States Duration: Sep 22 2002 → Sep 25 2002

Other

Other	International Conference on Image Processing (ICIP'02)
Country/Territory	United States
City	Rochester, NY
Period	9/22/02 → 9/25/02

ASJC Scopus subject areas

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

Cite this

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title = "Subband adaptive image denoising via bivariate shrinkage",

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.",

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AB - 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.

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M3 - Paper

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T2 - International Conference on Image Processing (ICIP'02)

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Subband adaptive image denoising via bivariate shrinkage

Abstract

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ASJC Scopus subject areas

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