Speckle noise reduction
Speckle noise reduction
Speckle noise reduction
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<strong>Speckle</strong> <strong>noise</strong> <strong>reduction</strong>: a review – Ph. Courmontagne<br />
15<br />
Adaptive Filters in Transform Domain<br />
‣ Wavelet expansion & soft or hard thresholding<br />
The SAS data is expanded into several complementary sub-spaces (Mallat<br />
[Mallat, 1989], à Trous [Holdschneider, 1989])<br />
Only a few part of the wavelet coefficients are kept (soft and hard<br />
thresholding [Donoho, 1993])<br />
Only available for a Gaussian disturbing signal Ridgelet [Candes, 1998],<br />
Curvelet [Starck, 2002] and Gaussianisation [Mallet, 2000]<br />
‣ Stochastic Matched Filter (SMF, [Cavassilas, 1991])<br />
SAS data expansion onto a basis enhancing the SNR; signal approximation<br />
reconstruction using only a few part of the decomposition coefficients<br />
Mean square error minimization [Chaillan1, 2005], speckle <strong>noise</strong> local<br />
statistics [Courmontagne, 2007], adaptive [Courmontagne, 2006]<br />
Coupled with multi-resolution analysis: à Trous algorithm [Chaillan, 2006],<br />
Mallat algorithm [Chaillan2, 2005]
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