Open AccessSynthetic aperture radar image despeckling via total generalised variation approach
Author(s) -
Feng Wensen,
Lei Hong,
Qiao Hong
Publication year - 2015
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2013.0701
Subject(s) - synthetic aperture radar , variation (astronomy) , artificial intelligence , computer science , radar imaging , image (mathematics) , computer vision , radar , remote sensing , pattern recognition (psychology) , geology , telecommunications , physics , astrophysics
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.