Wavelet and multirate denoising for signal-dependent noise

Bruno Aiazzi; Luciano Alparone; Stefano Baronti; Andrea Garzelli

doi:10.1117/12.408674

4 December 2000 Wavelet and multirate denoising for signal-dependent noise

Bruno Aiazzi, Luciano Alparone, Stefano Baronti, Andrea Garzelli

Proceedings Volume 4119, Wavelet Applications in Signal and Image Processing VIII; (2000) https://doi.org/10.1117/12.408674
Event: International Symposium on Optical Science and Technology, 2000, San Diego, CA, United States

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.

Citation Download Citation

Bruno Aiazzi, Luciano Alparone, Stefano Baronti, and Andrea Garzelli "Wavelet and multirate denoising for signal-dependent noise", Proc. SPIE 4119, Wavelet Applications in Signal and Image Processing VIII, (4 December 2000); https://doi.org/10.1117/12.408674

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