SAR Image De-Noising based on GNL-Means with Optimized Pixel-Wise Weighting in Non-Subsample Shearlet Domain | Zendy

Shuaiqi Liu | Zendy; Yu Zhang | Zendy; Qi Hu | Zendy; Ming Liu | Zendy; Jie Zhao | Zendy

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SAR Image De-Noising based on GNL-Means with Optimized Pixel-Wise Weighting in Non-Subsample Shearlet Domain

Author(s) -

Shuaiqi Liu,

Yu Zhang,

Qi Hu,

Ming Liu,

Jie Zhao

Publication year - 2016

Publication title -

computer and information science

Language(s) - English

Resource type - Journals

eISSN - 1913-8997

pISSN - 1913-8989

DOI - 10.5539/cis.v10n1p16

Subject(s) - weighting , computer science , artificial intelligence , pixel , speckle pattern , image (mathematics) , shearlet , computer vision , pattern recognition (psychology) , medicine , radiology

SAR images have been widely used in many fields such as military and remote sensing. So the suppression of the speckle has been an important research issues. To improve the visual effect of non-local means, generalized non-local (GNL) means with optimized pixel-wise weighting is applied to shrink the coefficients of non-subsample Shearlet transform (NSST) of SAR image. The new method can optimize the weight of GNL, which not only improve the PSNR of de-noised image, but also can significantly enhance the visual effect of de-noising image.

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