Synthetic aperture radar image despeckling via total generalised variation approach. Issue 3 (1st March 2015)

• Record Type: Journal Article
• Authors: Feng, Wensen; Lei, Hong; Qiao, Hong
• Is Part Of: IET image processing. Volume 9:Issue 3(2015)
• Page Start: 236
• Page End: 248
• Publication Date: 2015-03-01
• Subjects: synthetic aperture radar -- radar imaging -- image denoising -- speckle -- variational techniques -- piecewise constant techniques -- smoothing methods -- higher order statistics -- optimisation -- Monte Carlo methods; synthetic aperture radar image despeckling -- total generalised variation approach -- speckle reduction -- total variation regularisation -- piecewise constant image -- smooth transition -- TGV‐based model -- staircasing artefact reduction -- higher order smoothness -- numerical scheme -- Nesterov algorithm -- TGV‐based optimisation problem -- Monte Carlo method; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2013.0701
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Speckle reduction is an important task in synthetic aperture radar. One extensively used approach is based on total variation (TV) regularisation, which can realise significantly sharp edges, but on the other hand brings in the undesirable staircasing artefacts. In essence, the TV‐based methods tend to create piecewise‐constant images even in regions with smooth transitions. In this study, a new method is proposed for speckle reduction via total generalised variation (TGV) penalty. This is reasonable from the fact that the TGV‐based model can reduce the staircasing artefacts of TV by being aware of higher‐order smoothness. An efficient numerical scheme based on the Nesterov's algorithm is also developed for solving the TGV‐based optimisation problem. Monte Carlo experiments show that the proposed scheme yields state‐of‐the‐art results in terms of both performance and speed. Especially when the image has some higher‐order smoothness, the authors' scheme outperforms the TV‐based methods.