Stripe noise removal of remote sensing image with a directional l0 sparse model
Hong-Xia Dou, Ting‐Zhu Huang, Liang-Jian Deng, Yong Chen · 2017
This paper commits to remove the stripe noise to enhance the visual quality of remote sensing images, in the meanwhile preserves image details of stripe-free regions. Instead of solving the underlying image as most of researches, we propose a non-convex l0model for remote sensing image destriping by taking full consideration of the intrinsically directional and structural priors of stripe noise. Moreover, the proposed non-convex model can be solved by the proximal alternating direction method of multipliers (PADMM) method which theoretically guarantees converging to a KKT point. Extensively experimental results on simulated and real data demonstrate that the proposed method outperforms recent state-of-the-art destriping methods, both visually and quantitatively.