Multiplivative matching pursuit

Author

Serir, Amina ; Pesquet, Jean Christophe

Author_Institution

LTI Electron. Inst., USTHB, Alger, Algeria

Volume

6

fYear

2000

fDate

2000

Firstpage

1935

Abstract

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

Keywords

convergence of numerical methods; filtering theory; image representation; noise; nonlinear filters; radar imaging; speckle; synthetic aperture radar; SAR images; convergence; multiplicative matching pursuit decomposition; multiplicative noisy image; nonlinear filter; nonlinear low-level image representation; nonlinear waveform expansion; redundant functions dictionary; signal local structures; signal-dependent noise; speckle reduction; Adaptive filters; Additive noise; Convergence; Dictionaries; Matching pursuit algorithms; Pixel; Pursuit algorithms; Speckle; Testing; Wavelet packets;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 2000. ICASSP '00. Proceedings. 2000 IEEE International Conference on

Conference_Location

Istanbul

ISSN

1520-6149

Print_ISBN

0-7803-6293-4

Type

conf

DOI

10.1109/ICASSP.2000.859208

Filename

859208