SAR image despeckling via Lp norm regularization

Synthetic aperture radar(SAR)image despeckling has been an attractive problem in remote sensing.The main challenge is to suppress speckle while preserving edges and preventing unnatural artifacts(such as annoying artifacts in homogeneous regions and over-smoothed edges).To address these problems,this paper proposes a new variational model with a nonconvex nonsmooth Lp(0＜p＜1)norm regularization.It incorporates Lp(0＜p＜1)norm regularization and I-divergence fideli-ty term.Due to the nonconvex nonsmooth property,the regularization can better recover neat edges and homogeneous regions.The I-divergence fidelity term is used to suppress the multiplicative noise effectively.Moreover,based on variable-splitting and alternating direction method of multipliers(ADMM)method,an efficient algorithm is proposed for solving this model.Intensive experimental results demonstrate that nonconvex nonsmooth model is superior to other state-of-the-art approaches qualitatively and quantitatively.