Multiplivative matching pursuit

This paper introduces a novel nonlinear low-level representation of an image with signal-dependent noise. For multiplicative noisy image, we introduce an algorithm called multiplicative matching pursuit decomposition (MMPD), that decomposes the signal containing the intrinsic variation into a nonlinear expansion of waveforms that are selected from a redundant dictionary of functions. These waveforms are chosen in order to best match the signal local structures. The convergence of this new multiplicative decomposition has been proved and tested in practice. An application to speckle reduction in SAR images is described