Abstract
AbstractAlmost every digital image is unavoidably contaminated by various noise sources. In our previous paper, we focused on Gaussian and Poisson noises. Unlike additive Gaussian noise, Poisson noise is signal-dependent and separating signal from noise is a difficult task. A wavelet-based maximum likelihood method for Bayesian estimator that recovers the signal component of the wavelet coefficients in original images by using an alpha-stable signal prior distribution is demonstrated to the discussed noise removal. Current paper is to extend out previous results to more complex cases that noises comprised of compound Poisson, Gaussian, and impulse noises via Lévy process analysis. As an example, an improved Bayesian estimator that is a natural extension of other wavelet denoising via a colour image is presented to illustrate our discussion.KeywordsWavelet CoefficientImpulse NoiseNoise RemovalBayesian EstimatorImpulsive NoiseThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Published Version
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