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- aggregation classification "A1".
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation date "2009".
- aggregation format "application/pdf".
- aggregation hasFormat 1002612.bibtex.
- aggregation hasFormat 1002612.csv.
- aggregation hasFormat 1002612.dc.
- aggregation hasFormat 1002612.didl.
- aggregation hasFormat 1002612.doc.
- aggregation hasFormat 1002612.json.
- aggregation hasFormat 1002612.mets.
- aggregation hasFormat 1002612.mods.
- aggregation hasFormat 1002612.rdf.
- aggregation hasFormat 1002612.ris.
- aggregation hasFormat 1002612.txt.
- aggregation hasFormat 1002612.xls.
- aggregation hasFormat 1002612.yaml.
- aggregation isPartOf urn:issn:1057-7149.
- aggregation language "eng".
- aggregation rights "I have transferred the copyright for this publication to the publisher".
- aggregation subject "Technology and Engineering".
- aggregation title "Image denoising using mixtures of projected Gaussian scale mixtures".
- aggregation abstract "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 dimensional approximation of the local neighbourhood is obtained, thereby modeling the strongest correlations in that neighbourhood. The model is a generalization of the recently developed Mixture of GSM (MGSM) model, that offers a significant improvement 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, even 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.".
- aggregation authorList BK730538.
- aggregation endPage "1702".
- aggregation issue "8".
- aggregation startPage "1689".
- aggregation volume "18".
- aggregation aggregates 1002651.
- aggregation isDescribedBy 1002612.
- aggregation similarTo TIP.2009.2022006.
- aggregation similarTo LU-1002612.