Open AccessSparse PDE for SAR image speckle suppression
Author(s) -
Wang Zelong,
Tan Xintong,
Yu Qi,
Zhu Jubo
Publication year - 2017
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.2016.0769
Subject(s) - speckle pattern , synthetic aperture radar , speckle noise , computer science , artificial intelligence , computer vision , sparse approximation , radar imaging , pattern recognition (psychology) , algorithm , radar , telecommunications
Speckle suppression is extremely important for understanding and utilising synthetic aperture radar (SAR) images, while the emphasis of the traditional methods for speckle suppression is usually focused on removing the noise instead of keeping the scattering character of imaging objects, which has caused serious interference to the subsequent applications of SAR images. The authors aim to develop sparse partial differential equation (PDE) for speckle suppression of SAR image, where the PDE model is associated with the sparse prior of objects and the statistical property of speckle. The PDE model has been proved to have the ability of denoising and edge‐preserving by proper design, and the sparse prior of the point‐ and line‐like objects on SAR images has been also illustrated, both of which help for keeping the scattering characters. To solve the proposed sparse PDE model, a numerical algorithm is designed and the sparse constraint is realised in each step of the diffusion process. In experiments, several real SAR images are utilised to validate the performance of the proposed method.