scholarly journals An Alternating Direction Method for Mixed Gaussian Plus Impulse Noise Removal

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Si Wang ◽  
Ting-Zhu Huang ◽  
Xi-le Zhao ◽  
Jun Liu
Keyword(s):  
Total Variation ◽  
Numerical Experiments ◽  
Impulse Noise ◽  
State Of The Art ◽  
Noise Removal ◽  
Alternating Direction Method ◽  
Impulse Noise Removal ◽  
Alternating Direction ◽  
Total Variation Model ◽  
Blurred Images

A combined total variation and high-order total variation model is proposed to restore blurred images corrupted by impulse noise or mixed Gaussian plus impulse noise. We attack the proposed scheme with an alternating direction method of multipliers (ADMM). Numerical experiments demonstrate the efficiency of the proposed method and the performance of the proposed method is competitive with the existing state-of-the-art methods.

scholarly journals Nonconvex regularization for blurred images with Cauchy noise

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Xiao Ai ◽  
Guoxi Ni ◽  
Tieyong Zeng
Keyword(s):  
Minimization Problem ◽  
Variational Approach ◽  
Numerical Experiments ◽  
Alternating Direction Method ◽  
Wavelet Frame ◽  
Alternating Direction ◽  
Nonconvex Regularization ◽  
Blurred Images ◽  
Different Levels

<p style='text-indent:20px;'>In this paper, we propose a nonconvex regularization model for images damaged by Cauchy noise and blur. This model is based on the method of the total variational proposed by Federica, Dong and Zeng [SIAM J. Imaging Sci.(2015)], where a variational approach for restoring blurred images with Cauchy noise is used. Here we consider the nonconvex regularization, namely a weighted difference of <inline-formula><tex-math id="M1">\begin{document}$ l_1 $\end{document}</tex-math></inline-formula>-norm and <inline-formula><tex-math id="M2">\begin{document}$ l_2 $\end{document}</tex-math></inline-formula>-norm coupled with wavelet frame, the alternating direction method of multiplier is carried out for this minimization problem, we describe the details of the algorithm and prove its convergence. Numerical experiments are tested by adding different levels of noise and blur, results show that our method can denoise and deblur the image better.</p>

scholarly journals A Modified Iterative Alternating Direction Minimization Algorithm for Impulse Noise Removal in Images

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Di Guo ◽  
Xiaobo Qu ◽  
Meng Wu ◽  
Keshou Wu
Keyword(s):  
Impulse Noise ◽  
Noise Removal ◽  
Nonlocal Operator ◽  
Salt And Pepper Noise ◽  
Self Similarity ◽  
Impulse Noise Removal ◽  
Alternating Direction ◽  
Continuation Algorithm ◽  
Simulation Results ◽  
Salt And Pepper

Images are often corrupted by impulse noise. In this paper, an alternating direction minimization with continuation algorithm is modified and iteratively used to remove the impulse noise in images by exploring its self-similarity. A patch-based nonlocal operator and sparse representation are married in thel1-l1optimization model to be solved. Simulation results demonstrate that the proposed algorithm outperforms typical denoising methods in terms of preserving edges and textures for both salt-and-pepper noise and random-valued impulse noise. It can be also applied to suppress impulse noise-like artifacts in real mural images.

scholarly journals Salt and Pepper Noise Removal with Multi-Class Dictionary Learning and L0 Norm Regularizations

Algorithms ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 7
Author(s):  
Di Guo ◽  
Zhangren Tu ◽  
Jiechao Wang ◽  
Min Xiao ◽  
Xiaofeng Du ◽  
...  
Keyword(s):  
Dictionary Learning ◽  
Impulse Noise ◽  
State Of The Art ◽  
Evaluation Criteria ◽  
Objective Evaluation ◽  
Noise Removal ◽  
Alternating Direction ◽  
Data Fidelity ◽  
Salt And Pepper ◽  
Image Details

Images may be corrupted by salt and pepper impulse noise during image acquisitions or transmissions. Although promising denoising performances have been recently obtained with sparse representations, how to restore high-quality images remains challenging and open. In this work, image sparsity is enhanced with a fast multiclass dictionary learning, and then both the sparsity regularization and robust data fidelity are formulated as minimizations of L0-L0 norms for salt and pepper impulse noise removal. Additionally, a numerical algorithm of modified alternating direction minimization is derived to solve the proposed denoising model. Experimental results demonstrate that the proposed method outperforms the compared state-of-the-art ones on preserving image details and achieving higher objective evaluation criteria.

Impulse Noise Removal Using Adaptive Radial Basis Function Interpolation

2016 ◽  
Vol 36 (3) ◽  
pp. 1192-1223 ◽  
Author(s):  
T. Veerakumar ◽  
Ravi Prasad K. Jagannath ◽  
Badri Narayan Subudhi ◽  
S. Esakkirajan
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Impulse Noise ◽  
Noise Removal ◽  
Radial Basis Function Interpolation ◽  
Impulse Noise Removal ◽  
Radial Basis
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