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• W2293343301 abstract "We study a class of smoothing filters for image denoising. Expressed as matrices, these smoothing filters must be row normalized so that each row sums to unity. Surprisingly, if one applies a column normalization to the matrix before the row normalization, the denoising quality can often be significantly improved. This column-row normalization corresponds to one iteration of a symmetrization process called the Sinkhorn-Knopp balancing algorithm. However, a complete understanding of the performance gain phenomenon is lacking. In this paper, we analyze the performance gain from a Gaussian mixture model (GMM) perspective. We show that the symmetrization is equivalent to an expectation-maximization (EM) algorithm for learning the GMM. Moreover, we make modifications to the symmetrization procedure and present a new denoising algorithm. Experimental results show that the new algorithm achieves comparable denoising results to some state-of-the-art methods." @default.
• W2293343301 created "2016-06-24" @default.
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• W2293343301 date "2015-09-01" @default.
• W2293343301 modified "2023-10-16" @default.
• W2293343301 title "Understanding symmetric smoothing filters via Gaussian mixtures" @default.
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• W2293343301 doi "https://doi.org/10.1109/icip.2015.7351252" @default.
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