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Blind image estimation through fuzzy matching pursuits
Aiazzi, B.; Baronti, S.; Alparone, L.;
Image Processing, 2001. Proceedings. 2001 International Conference on
Volume 1,
7-10 Oct. 2001
Page(s):241
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244 vol.1
Abstract:
This paper presents an original application of fuzzy logic to the 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 a series expansion of a finite number of 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 3 dB over Kuan's LLMMSE filtering and over 2 dB over wavelet thresholding, irrespective of noise model and intensity
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