WSEAS Transactions on Signal Processing
Print ISSN: 1790-5052, E-ISSN: 2224-3488
Volume 10, 2014
Low-Complexity Image Denoising via Analytical Form of Generalized Gaussian Random Vectors in AWGN
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Abstract: The application of the wavelet transform in image denoising has shown remarkable success over the last decade. In this paper, we present new Bayesian estimators for spherically-contoured generalized Gaussian (GG) random vectors in additive white Gaussian noise (AWGN). The derivations are an extension of existing results for Pearson type VII random vectors. In fact, Pearson type VII distribution have higher-order moment in statistical parameter for fitted the data such as mean, variance, and kurtosis. Indeed, where high-order statistics were used, better performance can be obtained but with much higher computational complexity. In Specific case, GG random vectors is similar to Pearson Type VII random vectors. However, the specific case of GG random vectors have only first few statistical moments such as variance. So, the proposed method can be calculated very fast, with out any contours. In our experiments, our proposed method gives promising denoising results with moderate complexity.
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Keywords: Bayesian Estimation, Spherically-Contoured Generalized Gaussian (GG) Random Vectors, Wavelet Denoising
Pages: 398-403
WSEAS Transactions on Signal Processing, ISSN / E-ISSN: 1790-5052 / 2224-3488, Volume 10, 2014, Art. #41