Locally Adaptive Wavelet-Based Image Denoising using the Gram-Charlier Prior Function

Statistical estimation techniques for the wavelet-based image denoising use suitable probability density functions (PDFs) as prior functions for the image coefficients. Due to the intrascale dependency of the local neighboring image wavelet coefficients, the prior functions are assumed to be station...

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Bibliographic Details
Main Authors: Rahman, S.M.M., Ahmad, M.O., Swamy, M.N.S.
Format: Conference Proceeding
Language:English
Subjects:
Bayesian methods
Compaction
Denoising
Discrete wavelet transforms
Gram-Charlier
Hidden Markov models
Image denoising
image wavelet coefficients
MAP estimator
Maximum a posteriori estimation
Noise reduction
Probability density function
Signal to noise ratio
Wavelet coefficients
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Summary:Statistical estimation techniques for the wavelet-based image denoising use suitable probability density functions (PDFs) as prior functions for the image coefficients. Due to the intrascale dependency of the local neighboring image wavelet coefficients, the prior functions are assumed to be stationary. In this paper, it is shown that the stationary Gram-Charlier (GC) PDF models the image coefficients better than the traditional ones, such as the stationary Gaussian and stationary generalized Gaussian PDFs. A Bayesian wavelet-based maximum a posteriori estimator is then developed by using the proposed GC prior function. Experimental results on standard images show that the proposed estimator provides a denoising performance, which is better than that of several existing denoising methods in terms of signal-to-noise ratio and visual quality.
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2007.4379368