Destriping of Remote Sensing Images by an Optimized Variational Model

• Published in: Sensors (Basel, Switzerland) Switzerland), 2023-08, Vol.23 (17), p.7529
• Main Authors: Yan, Fei, Wu, Siyuan, Zhang, Qiong, Liu, Yunqing, Sun, Haonan
• Format: Article
• Language: English
• Subjects: Algorithms; Decomposition; fast ADMM; Methods; Optimization; Remote sensing; remote sensing images; Sensors; Sparsity; stripe noise removal; variational model; ℓp quasinorm
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23177529

Satellite sensors often capture remote sensing images that contain various types of stripe noise. The presence of these stripes significantly reduces the quality of the remote images and severely affects their subsequent applications in other fields. Despite the existence of many stripe noise removal methods in the research, they often result in the loss of fine details during the destriping process, and some methods even generate artifacts. In this paper, we proposed a new unidirectional variational model to remove horizontal stripe noise. The proposed model fully considered the directional characteristics and structural sparsity of the stripe noise, as well as the prior features of the underlying image, to design different sparse constraints, and the ℓp quasinorm was introduced in these constraints to better describe these sparse characteristics, thus achieving a more excellent destriping effect. Moreover, we employed the fast alternating direction method of multipliers (ADMM) to solve the proposed non-convex model. This significantly improved the efficiency and robustness of the proposed method. The qualitative and quantitative results from simulated and real data experiments confirm that our method outperforms existing destriping approaches in terms of stripe noise removal and preservation of image details.