Demonstrates high-quality image restoration in multivariate analysis, suggesting improved denoising techniques.
We develop a novel multivariate Bayesian wavelet estimator of a simple analytical form that is computationally effective for the image denoising problem. The estimator is derived from the multivariate Laplacian model by using the maximum a posteriori rule. We find the multivariate estimator produces restoration results of high quality, both visually and in terms of peak signal-to-noise ratio.
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Tan et al. (2007) studied this question.
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