scholarly journals Stripe noise removal of remote sensing images by total variation regularization and group sparsity constraint

2017 ◽  
Vol 9 (6) ◽  
pp. 559 ◽  
Author(s):  
Yong Chen ◽  
Ting-Zhu Huang ◽  
Xi-Le Zhao ◽  
Liang-Jian Deng ◽  
Jie Huang
Keyword(s):  
Remote Sensing ◽  
Total Variation ◽  
Noise Removal ◽  
Total Variation Regularization ◽  
Group Sparsity ◽  
Remote Sensing Images ◽  
Sparsity Constraint

Deblurring of Remote Sensing Images Based on Constrained Total-Variation Regularization

2013 ◽  
Vol 50 (11) ◽  
pp. 111001 ◽  
Author(s):  
郭玲玲 Guo Lingling ◽  
张立国 Zhang Liguo ◽  
吴泽鹏 Wu Zepeng ◽  
任建岳 Ren Jianyue ◽  
张星祥 Zhang Xingxiang
Keyword(s):  
Remote Sensing ◽  
Total Variation ◽  
Total Variation Regularization ◽  
Remote Sensing Images

scholarly journals A Unidirectional Total Variation and Second-Order Total Variation Model for Destriping of Remote Sensing Images

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Min Wang ◽  
Ting-Zhu Huang ◽  
Xi-Le Zhao ◽  
Liang-Jian Deng ◽  
Gang Liu
Keyword(s):  
Remote Sensing ◽  
Total Variation ◽  
Second Order ◽  
Total Variation Regularization ◽  
Remote Sensing Images ◽  
Split Bregman ◽  
Directional Characteristic ◽  
Directional Information ◽  
Split Bregman Iteration ◽  
Large Width

Remote sensing images often suffer from stripe noise, which greatly degrades the image quality. Destriping of remote sensing images is to recover a good image from the image containing stripe noise. Since the stripes in remote sensing images have a directional characteristic (horizontal or vertical), the unidirectional total variation has been used to consider the directional information and preserve the edges. The remote sensing image contaminated by heavy stripe noise always has large width stripes and the pixels in the stripes have low correlations with the true pixels. On this occasion, the destriping process can be viewed as inpainting the wide stripe domains. In many works, high-order total variation has been proved to be a powerful tool to inpainting wide domains. Therefore, in this paper, we propose a variational destriping model that combines unidirectional total variation and second-order total variation regularization to employ the directional information and handle the wide stripes. In particular, the split Bregman iteration method is employed to solve the proposed model. Experimental results demonstrate the effectiveness of the proposed method.

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