Image and Signal Processing Methods

Using two coefficients modeling of nonsubsampled Shearlet transform for despeckling

[+] Author Affiliations
Saeed Jafari, Sedigheh Ghofrani

Islamic Azad University, Electrical and Electronic Engineering Department, Tehran South Branch, No. 9, 14th Street, Pakistan Street, Shahid Beheshti Street, Tehran 15317-64611, Iran

J. Appl. Remote Sens. 10(1), 015002 (Jan 19, 2016). doi:10.1117/1.JRS.10.015002
History: Received June 27, 2015; Accepted December 3, 2015
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Abstract.  Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise. Two approaches based on modeling the nonsubsampled Shearlet transform (NSST) coefficients are presented. Two-sided generalized Gamma distribution and normal inverse Gaussian probability density function have been used to model the statistics of NSST coefficients. Bayesian maximum a posteriori estimator is applied to the corrupted NSST coefficients in order to estimate the noise-free NSST coefficients. Finally, experimental results, according to objective and subjective criteria, carried out on both artificially speckled images and the true SAR images, demonstrate that the proposed methods outperform other state of art references via two points of view, speckle noise reduction and image quality preservation.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Saeed Jafari and Sedigheh Ghofrani
"Using two coefficients modeling of nonsubsampled Shearlet transform for despeckling", J. Appl. Remote Sens. 10(1), 015002 (Jan 19, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.015002


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