Selection of Thresholding Scheme for Image Noise Reduction on Wavelet Components Using Bayesian Estimation

Authors:
A. De Stefano;P. R. White;W. B. Collis
Affiliations:
ISVR, University of Southampton, Southampton, UK. ads@isvr.soton.ac.uk;ISVR, University of Southampton, Southampton, UK;The Foundry, London, UK
Venue:
Journal of Mathematical Imaging and Vision
Year:
2004

Citing 8
Cited 1

Wavelet analysis

IEEE Spectrum
Optimal wavelet thresholding for various coding schemes

ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 1)-Volume 1 - Volume 1
Analysis of multiresolution image denoising schemes using generalized Gaussian and complexity priors

IEEE Transactions on Information Theory
Wavelet thresholding via MDL for natural images

IEEE Transactions on Information Theory
De-noising by soft-thresholding

IEEE Transactions on Information Theory
Spatially adaptive wavelet-based multiscale image restoration

IEEE Transactions on Image Processing
Multiple wavelet threshold estimation by generalized cross validation for images with correlated noise

IEEE Transactions on Image Processing
Adaptive image restoration using a generalized Gaussian model for unknown noise

IEEE Transactions on Image Processing

Novel Quantitative Method for Spleen's Morphometry in Splenomegally

ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing

Quantified Score

Hi-index	0.00

Visualization

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.