scholarly journals An Edge-Driven Total Variation Approach to Image Deblurring and Denoising

2006 ◽  
Author(s):  
Hongwei Zheng ◽  
O. Hellwich
Keyword(s):  
Total Variation ◽  
Image Deblurring ◽  
Variation Approach ◽  
Deblurring And Denoising

An efficient non-convex total variation approach for image deblurring and denoising

2021 ◽  
Vol 397 ◽  
pp. 125977
Author(s):  
Jingjing Liu ◽  
Ruijie Ma ◽  
Xiaoyang Zeng ◽  
Wanquan Liu ◽  
Mingyu Wang ◽  
...  
Keyword(s):  
Total Variation ◽  
Image Deblurring ◽  
Variation Approach ◽  
Deblurring And Denoising

scholarly journals Performance of relaxed iterative methods for image deblurring problems

2019 ◽  
Vol 13 ◽  
pp. 174830261986173 ◽  
Author(s):  
Jae H Yun
Keyword(s):  
Fixed Point ◽  
Iterative Methods ◽  
Total Variation ◽  
Numerical Experiments ◽  
Krylov Subspace ◽  
Image Deblurring ◽  
Total Variation Regularization ◽  
L1 Norm ◽  
Split Bregman ◽  
Relaxation Parameters

In this paper, we consider performance of relaxation iterative methods for four types of image deblurring problems with different regularization terms. We first study how to apply relaxation iterative methods efficiently to the Tikhonov regularization problems, and then we propose how to find good preconditioners and near optimal relaxation parameters which are essential factors for fast convergence rate and computational efficiency of relaxation iterative methods. We next study efficient applications of relaxation iterative methods to Split Bregman method and the fixed point method for solving the L1-norm or total variation regularization problems. Lastly, we provide numerical experiments for four types of image deblurring problems to evaluate the efficiency of relaxation iterative methods by comparing their performances with those of Krylov subspace iterative methods. Numerical experiments show that the proposed techniques for finding preconditioners and near optimal relaxation parameters of relaxation iterative methods work well for image deblurring problems. For the L1-norm and total variation regularization problems, Split Bregman and fixed point methods using relaxation iterative methods perform quite well in terms of both peak signal to noise ratio values and execution time as compared with those using Krylov subspace methods.

The anti-reflective algebra: structural and computational analysis with application to image deblurring and denoising

CALCOLO ◽  
2008 ◽  
Vol 45 (3) ◽  
pp. 149-175 ◽  
Author(s):  
Antonio Aricò ◽  
Marco Donatelli ◽  
Stefano Serra-Capizzano
Keyword(s):  
Computational Analysis ◽  
Image Deblurring ◽  
Deblurring And Denoising

Adaptive total variation image deblurring: A majorization–minimization approach

2009 ◽  
Vol 89 (9) ◽  
pp. 1683-1693 ◽  
Author(s):  
João P. Oliveira ◽  
José M. Bioucas-Dias ◽  
Mário A.T. Figueiredo
Keyword(s):  
Total Variation ◽  
Image Deblurring ◽  
Adaptive Total Variation ◽  
Minimization Approach
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