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DC Field | Value | Language |
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dc.contributor.author | Gokul, J. | - |
dc.contributor.author | Nair, M.S. | - |
dc.contributor.author | Rajan, J. | - |
dc.date.accessioned | 2020-03-31T08:31:25Z | - |
dc.date.available | 2020-03-31T08:31:25Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Computers and Geosciences, 2017, Vol.109, , pp.16-24 | en_US |
dc.identifier.uri | http://idr.nitk.ac.in/jspui/handle/123456789/11449 | - |
dc.description.abstract | SAR 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 Ltd | en_US |
dc.title | Guided SAR image despeckling with probabilistic non local weights | en_US |
dc.type | Article | en_US |
Appears in Collections: | 1. Journal Articles |
Files in This Item:
File | Description | Size | Format | |
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5.Guided SAR image.pdf | 3.34 MB | Adobe PDF | ![]() View/Open |
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