A novel truncated nonconvex nonsmooth variational method for SAR image despeckling
Mingqiang Guo, Chengde Han, Weina Wang, Saishang Zhong, Ruina Lv, Zheng Liu
Abstract
Speckle reduction is a fundamental problem in coherent imaging systems. In this paper, to suppress the speckle in SAR images, we propose a novel truncated nonconvex nonsmooth model. It incorporates a truncated nonconvex regularization term and an I-divergence fidelity term. The truncated ℓpnorm (0<p<1) regularization can better recover neat edges and simultaneously prevent contrast reduction artefact. The I-divergence fidelity term is used to suppress the multiplicative noise effectively. We also propose an efficient algorithm based on variable-splitting and alternating direction method of multipliers (ADMM) method to solve the model. Compared to state-of-the-art speckle suppression methods, intensive experimental results on a variety of SAR images show the superiority of the proposed method qualitatively and quantitatively.