A Remote Sensing Image Destriping Model Based on Low-Rank and Directional Sparse Constraint

Remote Sensing

Hongsong Qu¹  Liangliang Zheng¹  Tan Gao¹  Ziyu Zhang¹  Xiaobin Wu¹ 

[1] Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China;

关键词: destriping;    low-rank;    sparse;    total variational (TV);    remote sensing;   

DOI  :  10.3390/rs13245126

来源: DOAJ

【 摘 要 】

Stripe noise is a common condition that has a considerable impact on the quality of the images. Therefore, stripe noise removal (destriping) is a tremendously important step in image processing. Since the existing destriping models cause different degrees of ripple effects, in this paper a new model, based on total variation (TV) regularization, global low rank and directional sparsity constraints, is proposed for the removal of vertical stripes. TV regularization is used to preserve details, and the global low rank and directional sparsity are used to constrain stripe noise. The directional and structural characteristics of stripe noise are fully utilized to achieve a better removal effect. Moreover, we designed an alternating minimization scheme to obtain the optimal solution. Simulation and actual experimental data show that the proposed model has strong robustness and is superior to existing competitive destriping models, both subjectively and objectively.

【 授权许可】

Unknown