Please use this identifier to cite or link to this item: https://idr.l1.nitk.ac.in/jspui/handle/123456789/11449
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dc.contributor.authorGokul, J.-
dc.contributor.authorNair, M.S.-
dc.contributor.authorRajan, J.-
dc.date.accessioned2020-03-31T08:31:25Z-
dc.date.available2020-03-31T08:31:25Z-
dc.date.issued2017-
dc.identifier.citationComputers and Geosciences, 2017, Vol.109, , pp.16-24en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/11449-
dc.description.abstractSAR images are generally corrupted by granular disturbances called speckle, which makes visual analysis and detail extraction a difficult task. Non Local despeckling techniques with probabilistic similarity has been a recent trend in SAR despeckling. To achieve effective speckle suppression without compromising detail preservation, we propose an improvement for the existing Generalized Guided Filter with Bayesian Non-Local Means (GGF-BNLM) method. The proposed method (Guided SAR Image Despeckling with Probabilistic Non Local Weights) replaces parametric constants based on heuristics in GGF-BNLM method with dynamically derived values based on the image statistics for weight computation. Proposed changes make GGF-BNLM method adaptive and as a result, significant improvement is achieved in terms of performance. Experimental analysis on SAR images shows excellent speckle reduction without compromising feature preservation when compared to GGF-BNLM method. Results are also compared with other state-of-the-art and classic SAR depseckling techniques to demonstrate the effectiveness of the proposed method. 2017 Elsevier Ltden_US
dc.titleGuided SAR image despeckling with probabilistic non local weightsen_US
dc.typeArticleen_US
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