Image despeckling with non-local total bounded variation regularization

Please use this identifier to cite or link to this item: https://idr.nitk.ac.in/jspui/handle/123456789/11523

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DC Field	Value	Language
dc.contributor.author	Jidesh, P.
dc.contributor.author	Banothu, B.
dc.date.accessioned	2020-03-31T08:35:17Z	-
dc.date.available	2020-03-31T08:35:17Z	-
dc.date.issued	2018
dc.identifier.citation	Computers and Electrical Engineering, 2018, Vol.70, , pp.631-646	en_US
dc.identifier.uri	http://idr.nitk.ac.in/jspui/handle/123456789/11523	-
dc.description.abstract	A non-local total bounded variational (TBV) regularization model is proposed for restoring images corrupted with data-correlated speckles and linear blurring artifacts. The energy functional of the model is derived using maximum a posteriori (MAP) estimate of the noise probability density function (PDF). The non-local total bounded variation prior regularizes the model while the data fidelity is derived using the MAP estimator of the noise PDF. The computational efficiency of the model is improved using a fast numerical scheme based on the Augmented Lagrange formulation. The proposed model is employed to restore ultrasound (US) and synthetic aperture radar (SAR) images, which are usually speckled and blurred. The numerical results are presented and compared. Furthermore, a detailed theoretical study of the model is performed in addition to the experimental analysis. 2017 Elsevier Ltd	en_US
dc.title	Image despeckling with non-local total bounded variation regularization	en_US
dc.type	Article	en_US
Appears in Collections:	1. Journal Articles

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