A New TV-Stokes Model with Augmented Lagrangian Method for Image Denoising and Deconvolution

2011 ◽  
Vol 51 (3) ◽  
pp. 505-526 ◽  
Author(s):  
Dai-Qiang Chen ◽  
Li-Zhi Cheng ◽  
Fang Su
Keyword(s):  
Image Denoising ◽  
Augmented Lagrangian ◽  
Augmented Lagrangian Method ◽  
Lagrangian Method ◽  
New Tv

scholarly journals An Adaptive Total Generalized Variation Model with Augmented Lagrangian Method for Image Denoising

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Chuan He ◽  
Changhua Hu ◽  
Xiaogang Yang ◽  
Huafeng He ◽  
Qi Zhang
Keyword(s):  
Image Denoising ◽  
Augmented Lagrangian ◽  
Regularization Parameter ◽  
Augmented Lagrangian Method ◽  
Lagrangian Method ◽  
Qualitative Assessment ◽  
Edge Preservation ◽  
Total Generalized Variation ◽  
Proposed Model ◽  
Generalized Variation

We propose an adaptive total generalized variation (TGV) based model, aiming at achieving a balance between edge preservation and region smoothness for image denoising. The variable splitting (VS) and the classical augmented Lagrangian method (ALM) are used to solve the proposed model. With the proposed adaptive model and ALM, the regularization parameter, which balances the data fidelity and the regularizer, is refreshed with a closed form in each iterate, and the image denoising can be accomplished without manual interference. Numerical results indicate that our method is effective in staircasing effect suppression and holds superiority over some other state-of-the-art methods both in quantitative and in qualitative assessment.

scholarly journals Augmented Lagrangian method for a mean curvature based image denoising model

2013 ◽  
Vol 7 (4) ◽  
pp. 1409-1432 ◽  
Author(s):  
Wei Zhu ◽  
◽  
Xue-Cheng Tai ◽  
Tony Chan ◽  
◽  
...  
Keyword(s):  
Image Denoising ◽  
Mean Curvature ◽  
Augmented Lagrangian ◽  
Augmented Lagrangian Method ◽  
Lagrangian Method

scholarly journals An augmented Lagrangian method for solving total variation (TV)-based image registration model

2020 ◽  
Vol 14 ◽  
pp. 174830262097353
Author(s):  
Noppadol Chumchob ◽  
Ke Chen
Keyword(s):  
Image Registration ◽  
Augmented Lagrangian ◽  
Numerical Solutions ◽  
Augmented Lagrangian Method ◽  
Closed Form Solution ◽  
Numerical Algorithms ◽  
Lagrangian Method ◽  
Form Solution ◽  
The Other ◽  
Other Hand

Variational methods for image registration basically involve a regularizer to ensure that the resulting well-posed problem admits a solution. Different choices of regularizers lead to different deformations. On one hand, the conventional regularizers, such as the elastic, diffusion and curvature regularizers, are able to generate globally smooth deformations and generally useful for many applications. On the other hand, these regularizers become poor in some applications where discontinuities or steep gradients in the deformations are required. As is well-known, the total (TV) variation regularizer is more appropriate to preserve discontinuities of the deformations. However, it is difficult in developing an efficient numerical method to ensure that numerical solutions satisfy this requirement because of the non-differentiability and non-linearity of the TV regularizer. In this work we focus on computational challenges arising in approximately solving TV-based image registration model. Motivated by many efficient numerical algorithms in image restoration, we propose to use augmented Lagrangian method (ALM). At each iteration, the computation of our ALM requires to solve two subproblems. On one hand for the first subproblem, it is impossible to obtain exact solution. On the other hand for the second subproblem, it has a closed-form solution. To this end, we propose an efficient nonlinear multigrid (NMG) method to obtain an approximate solution to the first subproblem. Numerical results on real medical images not only confirm that our proposed ALM is more computationally efficient than some existing methods, but also that the proposed ALM delivers the accurate registration results with the desired property of the constructed deformations in a reasonable number of iterations.

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