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Mixed Noise Removal with Pareto Prior

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Document pages: 13 pages

Abstract: Denoising images contaminated by the mixture of additive white Gaussian noise(AWGN) and impulse noise (IN) is an essential but challenging problem. Thepresence of impulsive disturbances inevitably affects the distribution ofnoises and thus largely degrades the performance of traditional AWGN denoisers.Existing methods target to compensate the effects of IN by introducing aweighting matrix, which, however, is lack of proper priori and thus hard to beaccurately estimated. To address this problem, we exploit the Paretodistribution as the priori of the weighting matrix, based on which an accurateand robust weight estimator is proposed for mixed noise removal. Particularly,a relatively small portion of pixels are assumed to be contaminated with IN,which should have weights with small values and then be penalized out. Thisphenomenon can be properly described by the Pareto distribution of type 1.Therefore, armed with the Pareto distribution, we formulate the problem ofmixed noise removal in the Bayesian framework, where nonlocal self-similaritypriori is further exploited by adopting nonlocal low rank approximation.Compared to existing methods, the proposed method can estimate the weightingmatrix adaptively, accurately, and robust for different level of noises, thuscan boost the denoising performance. Experimental results on widely used imagedatasets demonstrate the superiority of our proposed method to thestate-of-the-arts.

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