Paper Info
Title
SAR image denoising using the non-subsampled contourlet transform and morphological operators
Abstract
This paper introduces a novel algorithm that combines the Non-Subsampled Contourlet Transform (NSCT) and morphological operators to reduce the multiplicative noise of synthetic aperture radar images. The image corrupted by multiplicative noise is preprocessed and decomposed into several scales and directions using the NSCT. Then, the contours and uniform regions of each subband are separated from noise. Finally, the resulting denoised subbands are transformed back into the spatial domain and applied the exponential function to obtain the denoised image. Experimental results show that the proposed method drastically reduces the multiplicative noise and outperforms other denoising methods, while achieving a better preservation of the visual details.
Year
DOI
Venue
2010
10.1007/978-3-642-16761-4_30
MICAI
Keywords
Field
DocType
better preservation,multiplicative noise,denoised image,denoising method,non-subsampled contourlet transform,morphological operator,denoised subbands,sar image,synthetic aperture radar image,non-subsampled contourlet,exponential function,synthetic aperture radar,speckle noise
Noise reduction,Computer vision,Exponential function,Pattern recognition,Synthetic aperture radar,Computer science,Operator (computer programming),Artificial intelligence,Image denoising,Speckle noise,Contourlet,Multiplicative noise
Conference
Volume
ISSN
ISBN
6437
0302-9743
3-642-16760-8
Citations 
PageRank 
References 
0
0.34
6
Authors
7
Name
Order
Citations
PageRank
José Manuel Mejía Muñoz142.20
Humberto de Jesús Ochoa Domínguez2234.82
Leticia Ortega Máynez3183.96
Osslan Osiris Vergara Villegas4156.40
Vianey Guadalupe Cruz Sánchez5277.24
Nelly Gordillo Castillo611.02
Efrén David Gutiérrez Casas700.34