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) 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 lowpass images, which are tailored to the noise model in order to mitigate its signal-dependence, or by thresholding a normalized nonredundant 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 denoising by soft-thresholding.
Wavelet and multirate denoising for signal-dependent noise / Aiazzi, Bruno; Alparone, Luciano; Baronti, Stefano; Garzelli, Andrea. - STAMPA. - 4119:(2000), pp. 843-852. (Intervento presentato al convegno Wavelet Applications in Signal and Image Processing VIII tenutosi a San Diego, CA, USA nel 31 July - 4 August 2000) [10.1117/12.408674].
Wavelet and multirate denoising for 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) 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 lowpass images, which are tailored to the noise model in order to mitigate its signal-dependence, or by thresholding a normalized nonredundant 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 denoising by soft-thresholding.
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