In this paper, after reviewing a general model to deal with signal-dependent image noise, the well known Local Linear Minimum Mean Squared Error (LLMMSE) Kuan's filter is derived for the most general case. Signal-dependent noise filtering is approached in a multiresolution framework either by LLMMSE processing ratios of combinations of low-pass images, which are tailored to the noise model in order to mitigate its signal-dependence, or by thresholding a normalized non-redundant wavelet transform designed to yield signal-independent noisy coefficients as well. Experimental results demonstrate that the Laplacian pyramid approach largely outperform LLMMSE filtering on a unique scale and is still superior to wavelet de-noising by thresholding.
Wavelet and pyramid filtering of signal-dependent noise / Aiazzi, Bruno; Alparone, Luciano; Baronti, Stefano. - ELETTRONICO. - 2000:(2000), pp. 1-4. ( 2000 10th European Signal Processing Conference, EUSIPCO 2000 fin 2000).
Wavelet and pyramid filtering of signal-dependent noise
ALPARONE, LUCIANO;
2000
Abstract
In this paper, after reviewing a general model to deal with signal-dependent image noise, the well known Local Linear Minimum Mean Squared Error (LLMMSE) Kuan's filter is derived for the most general case. Signal-dependent noise filtering is approached in a multiresolution framework either by LLMMSE processing ratios of combinations of low-pass images, which are tailored to the noise model in order to mitigate its signal-dependence, or by thresholding a normalized non-redundant wavelet transform designed to yield signal-independent noisy coefficients as well. Experimental results demonstrate that the Laplacian pyramid approach largely outperform LLMMSE filtering on a unique scale and is still superior to wavelet de-noising by thresholding.
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