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Darius Andriukaitis
Kaunas University of Technology, Lithuania

Alexander Argyros
The University of Sydney, Australia

Radu Arsinte
Technical University of Cluj Napoca, Romania

Ivan Baronak
Slovak University of Technology, Slovakia

Khosrow Behbehani
The University of Texas at Arlington, United States

Mohamed El Hachemi Benbouzid
University of Brest, France

Dalibor Biolek
University of Defence, Czech Republic

Klara Capova
University of Zilina, Slovakia

Erik Chromy
UPC Broadband Slovakia, Slovakia

Milan Dado
University of Zilina, Slovakia

Petr Drexler
Brno University of Technology, Czech Republic

Eva Gescheidtova
Brno University of Technology, Czech Republic

Ray-Guang Cheng
National Taiwan University of Science and Technology, Taiwan, Province of China

Gokhan Hakki Ilk
Ankara University, Turkey

Janusz Jezewski
Institute of Medical Technology and Equipment, Poland

Rene Kalus
VSB - Technical University of Ostrava, Czech Republic

Ivan Kasik
Academy of Sciences of the Czech Republic, Czech Republic

Jan Kohout
University of Defence, Czech Republic

Ondrej Krejcar
University of Hradec Kralove, Czech Republic

Miroslaw Luft
Technical University of Radom, Poland

Stanislav Marchevsky
Technical University of Kosice, Slovakia

Byung-Seo Kim
Hongik University, Korea

Valeriy Arkhin
Buryat State University, Russia

Nguyen Truong Khang
Van Lang University, Vietnam

Rupak Kharel
University of Huddersfield, United Kingdom

Fayaz Hussain
Ton Duc Thang University, Vietnam

Peppino Fazio
Ca’ Foscari University of Venice, Italy

Fazel Mohammadi
University of New Haven, United States of America

Thang Trung Nguyen
Ton Duc Thang University, Vietnam

Le Anh Vu
Ton Duc Thang University, Vietnam

Miroslav Voznak
VSB - Technical University of Ostrava, Czech Republic

Nguyen Huu Khanh Nhan
Ton Duc Thang University, Vietnam

Zbigniew Leonowicz
Wroclaw University of Science and Technology, Poland

Wasiu Oyewole Popoola
The University of Edinburgh, United Kingdom

Yuriy S. Shmaliy
Guanajuato University, Mexico

Lorand Szabo
Technical University of Cluj Napoca, Romania

Tran Trung Duy
Posts and Telecommunications Institute of Technology, Ho Chi Minh City, Vietnam

Xingwang Li
Henan Polytechnic University, China

Huynh Van Van
Ton Duc Thang University, Vietnam

Lubos Rejfek
University of Pardubice, Czech Republic

Neeta Pandey
Delhi Technological University, India

Huynh The Thien
Ho Chi Minh City University of Technology and Education, Vietnam

Mauro Tropea
DIMES Department of University of Calabria, Italy

Gaojian Huang
Henan Polytechnic University, China

Nguyen Quang Sang
Ho Chi Minh City University of Transport, Vietnam

Anh-Tu Le
Ho Chi Minh City University of Transport, Vietnam

Phu Tran Tin
Ton Duc Thang University, Vietnam

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Amit Garg, Anshika Goel

DOI: 10.15598/aeee.v21i3.4814


Synthetic Aperture Radar (SAR) is widely used for producing high quality imaging of Earth surface due to its capability of image acquisition in all-weather conditions. However, one limitation of SAR image is that image textures and fine details are usually contaminated with speckle noise. This noise is multiplicative and having granular pattern. It is caused due to the integration of various backscattered signals obtained during SAR image acquisition. It degrades the quality of SAR image and causes difficulty in visual interpretation. This paper presents a speckle reduction technique of SAR images based on statistical modelling of detail band shearlet coefficients (SC) in homomorphic environment. Modelling of SC obtained from log transformed noiseless SAR image are carried out as Normal Inverse Gaussian (NIG) distribution. However, SC pertaining to speckle noise are modelled as Gaussian distribution. These SC are segmented as heterogeneous, strongly heterogeneous and homogeneous regions depending upon the local statistics of SAR image. Then maximum a posteriori (MAP) estimation is employed over the SC of all regions of detail bands that belong to homogenous and heterogenous category. Objective and subjective quality assessment is performed for synthetic images and real SAR images. Reference image quality metrics, PSNR and SSIM for synthetic images and non-reference image quality metrics, ENL for real SAR images signify the potential of proposed method in comparison to six existing image denoising methods.


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