Multiplicative multiresolution decomposition for 2D signals: application to speckle reduction in SAR images

The new concept of multiplicative multiresolution decomposition (MMD) with perfect reconstruction is presented. This kind of analysis/synthesis representation is suitable for images corrupted by multiplicative noise, e.g. synthetic aperture radar (SAR) images. The design and implementation of a nonlinear multiplicative filter bank are addressed. An application to speckle reduction in SAR images is also described. The performance of the MMD speckle reduction method is studied and compared with those of the most often used adaptive filters.

[1]  Arnaud Doucet,et al.  Sequential Bayesian wavelet denoising , 1999, ISSPA '99. Proceedings of the Fifth International Symposium on Signal Processing and its Applications (IEEE Cat. No.99EX359).

[2]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[3]  Amina Serir,et al.  Satellite image multiscale representation by matching pursuit , 1995, Other Conferences.

[4]  Patrick Wambacq,et al.  Speckle filtering of synthetic aperture radar images : a review , 1994 .

[5]  Langis Gagnon,et al.  Speckle noise reduction of airborne SAR images with symmetric Daubechies wavelets , 1996, Defense, Security, and Sensing.

[6]  Dirk H. Hoekman.  Speckle ensemble statistics of logarithmically scaled data [radar] , 1991, IEEE Trans. Geosci. Remote. Sens..

[7]  Alexander A. Sawchuk,et al.  Adaptive restoration of images with speckle , 1987, IEEE Trans. Acoust. Speech Signal Process..

[8]  A. Belouchrani,et al.  Multiplicative multiresolution decomposition with perfect reconstruction , 2003, Proceedings of the 3rd IEEE International Symposium on Signal Processing and Information Technology (IEEE Cat. No.03EX795).