A modified total variation regularization approach based on the Gauss-Newton algorithm and split Bregman iteration for magnetotelluric inversion

2020 ◽  
Vol 178 ◽  
pp. 104073
Author(s):  
Deshan Feng ◽  
Xuan Su ◽  
Xun Wang ◽  
Xiangyu Wang
Keyword(s):  
Total Variation ◽  
Total Variation Regularization ◽  
Bregman Iteration ◽  
Split Bregman ◽  
Newton Algorithm ◽  
Split Bregman Iteration ◽  
Magnetotelluric Inversion

Removal of Glass Reflection Using TV-Retinex Model

2014 ◽  
Vol 511-512 ◽  
pp. 475-480
Author(s):  
Ke Fei Cheng ◽  
Dan Ni Li
Keyword(s):  
Total Variation ◽  
Computational Approach ◽  
Total Variation Regularization ◽  
Bregman Iteration ◽  
Split Bregman ◽  
Split Bregman Iteration

The Retinex model is mainly used to removal of unfavorable illumination effects from images. In this paper, the Retinex model combined with the total variation regularization (TV-Retinex) is presented to removal of glass reflection that can be solved by a fast computational approach based on the split Bregman iteration. Experiments demonstrated that the proposed method can effectively reduce this kind of artifact as well as preserve the edge and detailed information.

scholarly journals An Efficient Variational Method for Image Restoration

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Jun Liu ◽  
Ting-Zhu Huang ◽  
Xiao-Guang Lv ◽  
Si Wang
Keyword(s):  
Image Restoration ◽  
Total Variation ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Total Variation Regularization ◽  
Split Bregman ◽  
Split Bregman Iteration ◽  
Imaging Science ◽  
Constrained Minimization Problem ◽  
Structure Similarity Index

Image restoration is one of the most fundamental issues in imaging science. Total variation regularization is widely used in image restoration problems for its capability to preserve edges. In this paper, we consider a constrained minimization problem with double total variation regularization terms. To solve this problem, we employ the split Bregman iteration method and the Chambolle’s algorithm. The convergence property of the algorithm is established. The numerical results demonstrate the effectiveness of the proposed method in terms of peak signal-to-noise ratio (PSNR) and the structure similarity index (SSIM).

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