A NOVEL REMOVAL METHOD FOR DENSE STRIPES IN REMOTE SENSING IMAGES
Xinxin Liu, Huanfeng Shen, Qiangqiang Yuan, Liangpei Zhang, Qing Cheng · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · 2016
Abstract. In remote sensing images, the common existing stripe noise always severely affects the imaging quality and limits the related subsequent application, especially when it is with high density. To well process the dense striped data and ensure a reliable solution, we construct a statistical property based constraint in our proposed model and use it to control the whole destriping process. The alternating direction method of multipliers (ADMM) is applied in this work to solve and accelerate the model optimization. Experimental results on real data with different kinds of dense stripe noise demonstrate the effectiveness of the proposed method in terms of both qualitative and quantitative perspectives.