Abstract
Synthetic aperture radar (SAR) imagery can provide useful information in a multitude of applications, including climate change, environmental monitoring, meteorology, high dimensional mapping, ship monitoring, or planetary exploration. In this paper, we investigate solutions to a number of inverse problems encountered in SAR imaging. We propose a convex proximal splitting method for the optimisation of a cost function that includes a non-convex Cauchy-based penalty. The convergence of the overall cost function optimisation is ensured through careful selection of model parameters within a forward-backward (FB) algorithm. The performance of the proposed penalty function is evaluated by solving three standard SAR imaging inverse problems, including super-resolution, image formation, and despeckling, as well as ship wake detection for maritime applications. The proposed method is compared to several methods employing classical penalty functions such as total variation ($TV$) and $L_1$ norms, and to the generalised minimax-concave (GMC) penalty. We show that the proposed Cauchy-based penalty function leads to better image reconstruction results when compared to the reference penalty functions for all SAR imaging inverse problems in this paper.
Original language | English |
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Number of pages | 17 |
Journal | arXiv |
Publication status | Unpublished - 1 May 2020 |
Keywords
- Non-convex regularisation
- Convex optimisation
- Cauchy proximal operator
- Inverse problems
- Denoising
- Image reconstruction