A modified Chambolle-Pock primal-dual algorithm for Poisson noise removal

CALCOLO ◽  
2020 ◽  
Vol 57 (3) ◽  
Author(s):  
Benxin Zhang ◽  
Zhibin Zhu ◽  
Zhijun Luo
Keyword(s):  
Noise Removal ◽  
Poisson Noise ◽  
Dual Algorithm ◽  
Primal Dual

scholarly journals Rician Noise Removal via a Learned Dictionary

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Jian Lu ◽  
Jiapeng Tian ◽  
Lixin Shen ◽  
Qingtang Jiang ◽  
Xueying Zeng ◽  
...  
Keyword(s):  
Image Processing ◽  
Noise Removal ◽  
Sparse Representations ◽  
Computational Results ◽  
Rician Noise ◽  
Noisy Image ◽  
Dual Algorithm ◽  
Proposed Model ◽  
Learned Dictionary ◽  
Primal Dual

This paper proposes a new effective model for denoising images with Rician noise. The sparse representations of images have been shown to be efficient approaches for image processing. Inspired by this, we learn a dictionary from the noisy image and then combine the MAP model with it for Rician noise removal. For solving the proposed model, the primal-dual algorithm is applied and its convergence is studied. The computational results show that the proposed method is promising in restoring images with Rician noise.

Total Variation Based Parameter-Free Model for Impulse Noise Removal

2017 ◽  
Vol 10 (1) ◽  
pp. 186-204 ◽  
Author(s):  
Federica Sciacchitano ◽  
Yiqiu Dong ◽  
Martin S. Andersen
Keyword(s):  
Total Variation ◽  
Impulse Noise ◽  
Regularization Parameter ◽  
Noise Removal ◽  
Phase Method ◽  
Second Phase ◽  
Dual Algorithm ◽  
Two Phase ◽  
Total Variation Minimization ◽  
Primal Dual

AbstractWe propose a new two-phase method for reconstruction of blurred images corrupted by impulse noise. In the first phase, we use a noise detector to identify the pixels that are contaminated by noise, and then, in the second phase, we reconstruct the noisy pixels by solving an equality constrained total variation minimization problem that preserves the exact values of the noise-free pixels. For images that are only corrupted by impulse noise (i.e., not blurred) we apply the semismooth Newton's method to a reduced problem, and if the images are also blurred, we solve the equality constrained reconstruction problem using a first-order primal-dual algorithm. The proposed model improves the computational efficiency (in the denoising case) and has the advantage of being regularization parameter-free. Our numerical results suggest that the method is competitive in terms of its restoration capabilities with respect to the other two-phase methods.

Study of a primal-dual algorithm for equality constrained minimization

2014 ◽  
Vol 59 (3) ◽  
pp. 405-433 ◽  
Author(s):  
Paul Armand ◽  
Joël Benoist ◽  
Riadh Omheni ◽  
Vincent Pateloup
Keyword(s):  
Constrained Minimization ◽  
Dual Algorithm ◽  
Primal Dual
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