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Framelet-based sparse regularization for uneven intensity correction of remote sensing images in a retinex variational framewor

发布日期:2016-12-01 18:24:46 阅读次数:[3118]次 作者:

核心提示:来源出版物: OPTIK

作者: Lan, X (Lan, Xia); Zuo, ZY (Zuo, Zhiyong); Shen, HF (Shen, Huanfeng); Zhang, LP (Zhang, Liangpei); Hu, J (Hu, Jing)
来源出版物: OPTIK  卷: 127  期: 3  页: 1184-1189  DOI: 10.1016/j.ijleo.2015.10.214  出版年: 2016
摘要: Correcting uneven intensity distribution from a single image has long been a challenging problem with remote sensing image. In this paper, an analysis-based sparse prior is employed in the retinex variational framework for the uneven intensity correction of remote sensing images. This sparse regularization model is used to adjust uneven intensity by regularizing the sparsity of the reflectance component under framelet transform. Furthermore, the alternating minimization algorithm and split Bregman methodare adopted to solve the framelet-based sparse regularization model. The experiments, with both simulated images and real-life images, show that the proposed model can effectively correct the uneven intensity distribution. (C) 2015 Elsevier GmbH. All rights reserved. 


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