Paper Title:
Sar Image Denoising Based on Multifractal and Regularity Analysis
  Abstract

Synthetic Aperture Radar (SAR) images are corrupted by multiplicative speckle noise, which is due to random interference of electromagnetic waves. The noise degrades the quality of images and makes them hard to be interpreted, analyzed and classified. It appears sensible to reduce speckle in SAR images, while the structural features and textural information are not lost. This paper applies the framework of multifractal and regularity analysis to SAR image enhancement and denoising. The method does not make explicit assumptions about the model of the noise, but rather supposes that image denoising is equivalent to increasing the Hölder exponent at each point. The image is characterized via its multifractal spectrum, which mode yields the most frequent Hölder exponent. This manipulation leads to a smooth image while preserving the useful information in the signal. In order to evaluate the restoration result, Equivalent Number of Look (ENL) and edge save index (ESI) are used as criterion. Better result is obtained when regularity increase equal 0.5 (δ=0.5).

  Info
Periodical
Chapter
Chapter 2: Microwaves Optics and Image
Edited by
David Wang
Pages
534-539
DOI
10.4028/www.scientific.net/KEM.500.534
Citation
Y. P. Chen, Y. Chen, L. Tong, "Sar Image Denoising Based on Multifractal and Regularity Analysis", Key Engineering Materials, Vol. 500, pp. 534-539, 2012
Online since
January 2012
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Price
$32.00
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