access icon free Synthetic aperture radar image despeckling via total generalised variation approach

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.

Inspec keywords: optimisation; radar imaging; speckle; piecewise constant techniques; variational techniques; synthetic aperture radar; image denoising; smoothing methods; Monte Carlo methods; higher order statistics

Other keywords: synthetic aperture radar image despeckling; TGV-based optimisation problem; speckle reduction; total variation regularisation; higher order smoothness; TGV-based model; Nesterov algorithm; staircasing artefact reduction; Monte Carlo method; total generalised variation approach; piecewise constant image; numerical scheme; smooth transition

Subjects: Optical, image and video signal processing; Monte Carlo methods; Optimisation techniques; Radar equipment, systems and applications

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