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Selection of thresholding scheme for image noise reduction on wavelet components using Bayesian estimation

Selection of thresholding scheme for image noise reduction on wavelet components using Bayesian estimation
Selection of thresholding scheme for image noise reduction on wavelet components using Bayesian estimation
Methods for image noise reduction based on wavelet analysis perform by first decomposing the image and then by applying non-linear compression functions on the wavelet components. The approach commonly used to reduce the noise is to threshold the absolute pixel values of the components. The thresholding functions applied are members of a family of functions defining a specific shape. This shape has a fundamental influence on the characteristics of the output image.
This work presents and tests an alternative shape deduced from statistical estimation. Optimal shapes are deduced using Bayesian theory and a new shape is defined to approximate them. The derivation of thresholding shapes is optimal in LMSE and MAP senses. The noise is assumed additive Gaussian and white (AWGN) and the components are assumed to have statistical distributions consistent with the real component distributions. The optimal shapes are then approximated by a scheme utilised in the noise reduction procedure. Results demonstrating the efficiency of the image noise reduction procedure are included in the work.
bayesian estimation, image noise reduction, wavelet analysis
225-33
De Stefano, A.
103547f3-163d-4670-8839-1799a638e653
White, P.R.
2dd2477b-5aa9-42e2-9d19-0806d994eaba
Collis, W.B.
961c2de6-3be9-419e-b2b5-b21bc9126598
De Stefano, A.
103547f3-163d-4670-8839-1799a638e653
White, P.R.
2dd2477b-5aa9-42e2-9d19-0806d994eaba
Collis, W.B.
961c2de6-3be9-419e-b2b5-b21bc9126598

De Stefano, A., White, P.R. and Collis, W.B. (2004) Selection of thresholding scheme for image noise reduction on wavelet components using Bayesian estimation. Journal of Mathematical Imaging and Vision, 21 (3), 225-33. (doi:10.1023/B:JMIV.0000043738.05389.74).

Record type: Article

Abstract

Methods for image noise reduction based on wavelet analysis perform by first decomposing the image and then by applying non-linear compression functions on the wavelet components. The approach commonly used to reduce the noise is to threshold the absolute pixel values of the components. The thresholding functions applied are members of a family of functions defining a specific shape. This shape has a fundamental influence on the characteristics of the output image.
This work presents and tests an alternative shape deduced from statistical estimation. Optimal shapes are deduced using Bayesian theory and a new shape is defined to approximate them. The derivation of thresholding shapes is optimal in LMSE and MAP senses. The noise is assumed additive Gaussian and white (AWGN) and the components are assumed to have statistical distributions consistent with the real component distributions. The optimal shapes are then approximated by a scheme utilised in the noise reduction procedure. Results demonstrating the efficiency of the image noise reduction procedure are included in the work.

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More information

Published date: 2004
Keywords: bayesian estimation, image noise reduction, wavelet analysis

Identifiers

Local EPrints ID: 27982
URI: http://eprints.soton.ac.uk/id/eprint/27982
PURE UUID: 0af7e8d5-bcc5-43bf-a8aa-676d39426a8e
ORCID for P.R. White: ORCID iD orcid.org/0000-0002-4787-8713

Catalogue record

Date deposited: 28 Apr 2006
Last modified: 20 Jul 2019 01:25

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Contributors

Author: A. De Stefano
Author: P.R. White ORCID iD
Author: W.B. Collis

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