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Image Denoising Using Mixtures of Projected Gaussian Scale Mixtures

Image Denoising Using Mixtures of Projected Gaussian Scale Mixtures,10.1109/TIP.2009.2022006,IEEE Transactions on Image Processing,Bart Goossens,Aleks

Image Denoising Using Mixtures of Projected Gaussian Scale Mixtures   (Citations: 3)
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We propose a new statistical model for image restoration in which neighbourhoods of wavelet subbands are modeled by a discrete mixture of linear projected Gaussian Scale Mixtures (MPGSM). In each projection, a lower dimen- sional approximation of the local neighbourhood is obtained, thereby modeling the strongest correlations in that neighbour- hood. The model is a generalization of the recently developed Mixture of GSM (MGSM) model, that offers a significant im- provement both in PSNR and visually compared to the current state-of-the-art wavelet techniques. However the computation cost is very high which hampers its use for practical purposes. We present a fast EM algorithm that takes advantage of the projection bases to speed up the algorithm. The results show that, when projecting on a fixed data-independent basis, eve n computational advantages with a limited loss of PSNR can be obtained with respect to the BLS-GSM denoising method, while data-dependent bases of Principle Components offer a higher denoising performance, both visually and in PSNR compared to the current wavelet-based state-of-the-art denoising methods. Index Terms—Image denoising, Bayesian estimation, Gaussian Scale Mixtures
Journal: IEEE Transactions on Image Processing , vol. 18, no. 8, pp. 1689-1702, 2009
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