Paper Info
Title
Combined Wavelet And Curvelet Denoising Of Sar Images
Abstract
Synthetic aperture radar (SAR) images are corrupted by speckle noise due to random interference of electromagnetic waves. The speckle degrades the quality of the images and makes interpretations, analysis and classifications of SAR images harder. Therefore. some speckle reduction is necessary prior to the processing of SAR images. The speckle noise can be modeled as multiplicative i.i.d. Rayleigh noise. Logarithmic transformation of SAR images convert the multiplicative noise models to additive noise. In this paper,. two combinations of time invariant wavelet and curvelet transforms will be used for denoising of SAR images. The first one is called the combined filtering algorithm (CFA). This method is based on a constrained optimization problem, both in the wavelet and curvelet domains. The second method is called the adaptive combined method (ACM) which uses the wavelet transform to denoise homogeneous areas and the curvelet transform to denoise areas with edges.
Year
DOI
Venue
2004
10.1109/IGARSS.2004.1370070
IGARSS 2004: IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM PROCEEDINGS, VOLS 1-7: SCIENCE FOR SOCIETY: EXPLORING AND MANAGING A CHANGING PLANET
Keywords
Field
DocType
wavelet transforms,radar imaging,image classification,image quality,logarithmic transformation,wavelet transform,speckle noise,synthetic aperture radar,electromagnetic waves,multiplicative noise
Noise reduction,Computer vision,Speckle pattern,Computer science,Synthetic aperture radar,Remote sensing,Artificial intelligence,Speckle noise,Multiplicative noise,Wavelet transform,Curvelet,Wavelet
Conference
ISSN
Citations 
PageRank 
2153-6996
13
0.82
References 
Authors
5
3
Name
Order
Citations
PageRank
B. B. Saevarsson1130.82
Johannes R. Sveinsson2115095.58
J. A. Benediktsson386083.81