Please use this identifier to cite or link to this item: https://idr.l3.nitk.ac.in/jspui/handle/123456789/11523
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dc.contributor.authorJidesh, P.
dc.contributor.authorBanothu, B.
dc.date.accessioned2020-03-31T08:35:17Z-
dc.date.available2020-03-31T08:35:17Z-
dc.date.issued2018
dc.identifier.citationComputers and Electrical Engineering, 2018, Vol.70, , pp.631-646en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/11523-
dc.description.abstractA 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 Ltden_US
dc.titleImage despeckling with non-local total bounded variation regularizationen_US
dc.typeArticleen_US
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