NDLI: SAR image despeckling using plug-and-play ADMM

Content Provider	IET Digital Library
Author	Baraha, Satyakam Sahoo, Ajit Kumar
Abstract	The presence of speckle, a granular structure, seriously affects the visual interpretability of the synthetic aperture radar (SAR) images. Recently, model-based reconstruction techniques that use alternating direction method of multipliers (ADMM) have been widely utilised for denoising problems. Owing to the modular structure of ADMM, it is very simple to implement and also enables one to plug in any off-the-shelf denoising algorithm. However, the major limitations of these methods are mostly they have been established for Gaussian noise and the selection of a regulariser for a specific forward model is unknown. In this study, the despeckling of SAR images is addressed in the presence of multiplicative noise such as Rayleigh and Poisson. A mean filter is introduced for images corrupted by Rayleigh-based speckle, which transforms it to Nakagami distributed speckle, to improve the performance. Maximum a posteriori-based estimation involving Nakagami and Poisson distributions are applied to plug-and-play ADMM (PnP ADMM) framework, which provides enhanced despeckling ability along with the preservation of important details. The convergence analysis of the proposed PnP ADMM for multiplicative noise is also provided. The simulation studies are carried out taking several parameters for visual quality and statistical assessment to justify the effectiveness of the proposed method.
Starting Page	1297
Ending Page	1309
Page Count	13
ISSN	17518784
Volume Number	14
e-ISSN	17518792
Issue Number	Issue 9, Sep (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/14/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2019.0609
Journal	IET Radar, Sonar & Navigation
Publisher Date	2020-03-19
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Denoising Problem Enhanced Despeckling Ability Filtering Method in Signal Processing Filtering Theory Gaussian Noise Granular Structure Image Denoising Image Reconstruction Maximum Likelihood Estimation Model-based Reconstruction Technique Modular Structure Multiplicative Noise Nakagami Distributed Speckle Off-the-shelf Denoising Algorithm Optical, Image And Video Signal Processing Play Plug-and-play ADMM Framework PnP ADMM Poisson Distribution Radar Equipment Radar Imaging Rayleigh-based Speckle SAR Image SAR Image Despeckling Specific Forward Model Speckle Statistics Synthetic Aperture Radar Synthetic Aperture Radar Image System And Application Visual Interpretability Visual Quality
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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