Combined higher order non-convex total variation with overlapping group sparsity for impulse noise removal

2021 ◽  
Author(s):  
Tarmizi Adam ◽  
Raveendran Paramesran ◽  
Yin Mingming ◽  
Kuru Ratnavelu
Keyword(s):  
Total Variation ◽  
Impulse Noise ◽  
Noise Removal ◽  
Higher Order ◽  
Group Sparsity ◽  
Impulse Noise Removal ◽  
Overlapping Group Sparsity

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 Impulse Noise Denoising Using Total Variation with Overlapping Group Sparsity and Lp-Pseudo-Norm Shrinkage

2018 ◽  
Vol 8 (11) ◽  
pp. 2317 ◽  
Author(s):  
Lingzhi Wang ◽  
Yingpin Chen ◽  
Fan Lin ◽  
Yuqun Chen ◽  
Fei Yu ◽  
...  
Keyword(s):  
Total Variation ◽  
Impulse Noise ◽  
Random Noise ◽  
Group Sparsity ◽  
L1 Norm ◽  
New Model ◽  
Staircase Effect ◽  
Overlapping Group Sparsity ◽  
The Difference ◽  
Tv Model

Models based on total variation (TV) regularization are proven to be effective in removing random noise. However, the serious staircase effect also exists in the denoised images. In this study, two-dimensional total variation with overlapping group sparsity (OGS-TV) is applied to images with impulse noise, to suppress the staircase effect of the TV model and enhance the dissimilarity between smooth and edge regions. In the traditional TV model, the L1-norm is always used to describe the statistics characteristic of impulse noise. In this paper, the Lp-pseudo-norm regularization term is employed here to replace the L1-norm. The new model introduces another degree of freedom, which better describes the sparsity of the image and improves the denoising result. Under the accelerated alternating direction method of multipliers (ADMM) framework, Fourier transform technology is introduced to transform the matrix operation from the spatial domain to the frequency domain, which improves the efficiency of the algorithm. Our model concerns the sparsity of the difference domain in the image: the neighborhood difference of each point is fully utilized to augment the difference between the smooth and edge regions. Experimental results show that the peak signal-to-noise ratio, the structural similarity, the visual effect, and the computational efficiency of this new model are improved compared with state-of-the-art denoising methods.

scholarly journals Total Variation with Overlapping Group Sparsity for Image Deblurring under Impulse Noise

PLoS ONE ◽  
2015 ◽  
Vol 10 (4) ◽  
pp. e0122562 ◽  
Author(s):  
Gang Liu ◽  
Ting-Zhu Huang ◽  
Jun Liu ◽  
Xiao-Guang Lv
Keyword(s):  
Total Variation ◽  
Impulse Noise ◽  
Image Deblurring ◽  
Group Sparsity ◽  
Overlapping Group Sparsity

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

Total variation with overlapping group sparsity for speckle noise reduction

2016 ◽  
Vol 216 ◽  
pp. 502-513 ◽  
Author(s):  
Jun Liu ◽  
Ting-Zhu Huang ◽  
Gang Liu ◽  
Si Wang ◽  
Xiao-Guang Lv
Keyword(s):  
Noise Reduction ◽  
Total Variation ◽  
Speckle Noise ◽  
Group Sparsity ◽  
Overlapping Group Sparsity ◽  
Speckle Noise Reduction
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