标题: Stripe Noise Separation and Removal in Remote Sensing Images by Consideration of the Global Sparsity and Local Variational Properties

作者: Liu, XX (Liu, Xinxin); Lu, XL (Lu, Xiliang); Shen, HF (Shen, Huanfeng); Yuan, QQ (Yuan, Qiangqiang); Jiao, YL (Jiao, Yuling); Zhang, LP (Zhang, Liangpei)

来源出版物: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 卷: 54 期: 5 页: 3049-3060 DOI: 10.1109/TGRS.2015.2510418 出版年: MAY 2016

摘要: Remote sensing images are often contaminated by varying degrees of stripes, which severely affects the visual quality and subsequent application of the data. Unlike with conventional methods, we achieve the destriping by separating the stripe component based on a full analysis of the various stripe properties. Under an optimization framework, an l(0)-norm-based regularization is used to characterize the global sparse distribution of the stripes. In addition, difference-based constraints are adopted to describe the local smoothness and discontinuity in the along-stripe and across-stripe directions, respectively. The alternating direction method of multipliers is applied to solve and accelerate the model optimization. Experiments with both simulated and real data demonstrate the effectiveness of the proposed model, in terms of both qualitative and quantitative perspectives.