http://www.cnr.it/ontology/cnr/individuo/prodotto/ID187080

Blind Estimation of Noisy Images via Fuzzy Matching-Pursuits (Contributo in atti di convegno)

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Blind Estimation of Noisy Images via Fuzzy Matching-Pursuits (Contributo in atti di convegno) (literal)

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Bruno Aiazzi; Stefano Baronti; Luciano Alparone (2001)
Blind Estimation of Noisy Images via Fuzzy Matching-Pursuits
in SMMSP 2001: International TICSP Workshop on Spectral Methods and Multirate Signal Processing, Pula, Croazia, 16-18 Giugno 2001
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http://ticsp.cs.tut.fi/index.php/Contents-Report-13#PART_VI_Applications (literal)

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Proceedings of SMMSP 2001: International TICSP Workshop on Spectral Methods and Multirate Signal Processing (literal)

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\"Nello Carrara\" IROE-CNR, Via Panciatichi, 64, 50127 Firenze, Italy \"Nello Carrara\" IROE-CNR, Via Panciatichi, 64, 50127 Firenze, Italy DET, University of Florence, Via S. Marta, 3, 50139 Firenze, Italy (literal)

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Abstract

This paper presents an original application of fuzzy logic to restoration of images affected by white noise, possibly nonstationary and/or signal dependent. Space-varying linear MMSE estimation is stated as a problem of matching pursuits, in which the estimator is obtained as an expansion in series of a finite number of non-orthogonal prototype estimators, fitting the spatial features of the different statistical classes encountered, e.g. edges and textures. Such estimators are calculated in a fuzzy fashion through an automatic training procedure. The space-varying coefficients of the expansion are stated as degrees of fuzzy membership of a pixel to each of the estimators. Besides the fact that neither \"a priori\" knowledge of the noise model is required nor a particular signal model is assumed, a performance comparison highlights the advantages of the proposed approach. Results on simulated noisy versions of Lenna show a steady SNR improvement of almost 2.5 dB over Kuan's LLMMSE filtering and of 2 dB over wavelet thresholding, irrespective of noise model and intensity. (literal)

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