SAR Image Denoising Using the Non-Subsampled Contour let Transform and Morphological Operators

被引:0
作者
Mejia Munoz, Jose Manuel [1 ]
Ochoa Dominguez, Humberto de Jesus [1 ]
Ortega Maynez, Leticia [1 ]
Vergara Villegas, Osslan Osiris [1 ]
Cruz Sanchez, Vianey Guadalupe [1 ]
Gordillo Castillo, Nelly [1 ]
Gutierrez Casas, Efren David [1 ]
机构
[1] Univ Autonoma Ciudad Juarez, Dept Ingn Elect & Comp, Ciudad Juarez 32310, Chihuahua, Mexico
来源
ADVANCES IN ARTIFICIAL INTELLIGENCE, MICAI 2010, PT I | 2010年 / 6437卷
关键词
SAR images; Non-Subsampled Contourlet Transform; SAR image denoising; speckle noise;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:337 / 347
页数:11
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