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- W2028596987 abstract "Goal of this paper is an evaluation of Bayesian estimators: Minimum Mean Square Error (MMSE), MinimumMean Absolute Error (MMAE) and Maximum A-posteriori Probability (MAP). Such estimations have beencarried out in the undecimated wavelet domain. Bayesian estimation requires probability density function (PDF)models for the wavelet coefficients of the reflectivity and of the signal-dependent noise. In this work severalcombination of PDFs will be assessed. Closed-form solutions for MMSE, MMAE and MAP have been derived,whenever possible; numerical solutions otherwise. Experimental results carried out on simulated noisy imagesevidence the cost-performance trade off of the different estimators in conjunction with PDF models. MAPestimation with generalized Gaussian (GG) PDF for wavelet coefficients of both reflectivity and signal-dependentnoise (GG - GG) yields best performances. MAP with Laplacian - Gaussian (L - G) is only 0.07 dB less performingthan MAP with GG - GG. However, the former admits a closed-form solution and its computational cost is morethan ten times lower than that of the latter. Results on true single look high-resolution Cosmo-SkyMed SARimages provided by Italian Space Agency (ASI), are presented and discussed." @default.
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- W2028596987 date "2010-10-07" @default.
- W2028596987 modified "2023-09-23" @default.
- W2028596987 title "An evaluation of Bayesian estimators and PDF models for despeckling in the undecimated wavelet domain" @default.
- W2028596987 doi "https://doi.org/10.1117/12.866189" @default.
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