scholarly journals Primal-dual method to smoothing TV-based model for image denoising

2016 ◽  
Vol 10 (4) ◽  
pp. 235-243 ◽  
Author(s):  
Zhanjiang Zhi ◽  
Baoli Shi ◽  
Yi Sun
Keyword(s):  
Image Processing ◽  
Convergence Analysis ◽  
Image Denoising ◽  
Total Variation ◽  
Dual Method ◽  
Splitting Scheme ◽  
Prior Model ◽  
Processing Problem ◽  
Proposed Model ◽  
Primal Dual

The total variation-based Rudin–Osher–Fatemi model is an effective and popular prior model in the image processing problem. Different to frequently using the splitting scheme to directly solve this model, we propose the primal dual method to solve the smoothing total variation-based Rudin–Osher–Fatemi model and give some convergence analysis of proposed method. Numerical implements show that our proposed model and method can efficiently improve the numerical results compared with the Rudin–Osher–Fatemi model.

scholarly journals An adaptive model combining a total variation filter and a fractional-order filter for image restoration

2019 ◽  
Vol 13 ◽  
pp. 174830181983305 ◽  
Author(s):  
Yafeng Yang ◽  
Donghong Zhao
Keyword(s):  
Image Restoration ◽  
Total Variation ◽  
Fractional Order ◽  
Gradient Algorithm ◽  
Remarkable Effect ◽  
Order Filter ◽  
Model Combining ◽  
Proposed Model ◽  
Primal Dual ◽  
Image Edges

In this paper, we propose a model that combines a total variation filter with a fractional-order filter, which can unite the advantages of the two filters, and has a remarkable effect in the protection of image edges and texture details; simultaneously, the proposed model can eliminate the staircase effect. In addition, the model improves the PSNR compared with the total variation filter and the fractional-order filter when removing noise. Zhu and Chan presented the primal-dual hybrid gradient algorithm and proved that it is effective for the total variation filter. On the basis of their work, we employ the primal-dual hybrid gradient algorithm to solve the combined model in this article. The final experimental results show that the new model and algorithm are effective for image restoration.

A Primal–Dual Method for Total-Variation-Based Wavelet Domain Inpainting

2012 ◽  
Vol 21 (1) ◽  
pp. 106-114 ◽  
Author(s):  
You-Wei Wen ◽  
R. H. Chan ◽  
A. M. Yip
Keyword(s):  
Total Variation ◽  
Dual Method ◽  
Wavelet Domain ◽  
Primal Dual

A Nonlinear Primal-Dual Method for Total Variation-Based Image Restoration

1999 ◽  
Vol 20 (6) ◽  
pp. 1964-1977 ◽  
Author(s):  
Tony F. Chan ◽  
Gene H. Golub ◽  
Pep Mulet
Keyword(s):  
Image Restoration ◽  
Total Variation ◽  
Dual Method ◽  
Primal Dual

scholarly journals Local and Nonlocal Steering Kernel Weighted Total Variation Model for Image Denoising

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 329 ◽  
Author(s):  
Rui Lai ◽  
Yiguo Mo ◽  
Zesheng Liu ◽  
Juntao Guan
Keyword(s):  
Image Denoising ◽  
Total Variation ◽  
Noise Suppression ◽  
Nonlocal Symmetry ◽  
Visual Appearance ◽  
Quantitative Indices ◽  
Proposed Model ◽  
Total Variation Model ◽  
Weighted Total Variation ◽  
Tv Model

To eliminate heavy noise and retain more scene details, we propose a structure-oriented total variation (TV) model based on data dependent kernel function and TV criterion for image denoising application. The innovative model introduces the weights produced from the local and nonlocal symmetry features involved in the image itself to pick more precise solutions in the TV denoising process. As a result, the proposed local and nonlocal steering kernel weighted TV model yields excellent noise suppression and structure-preserving performance. The experimental results verify the validity of the proposed model in objective quantitative indices and subjective visual appearance.

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