scholarly journals Fast Overlapping Group Sparsity Total Variation Image Denoising Based on Fast Fourier Transform and Split Bregman Iterations

2017 ◽  
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Image Denoising ◽  
Total Variation ◽  
Group Sparsity ◽  
Split Bregman ◽  
Overlapping Group Sparsity

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

Total variation with overlapping group sparsity for deblurring images under Cauchy noise

2019 ◽  
Vol 341 ◽  
pp. 128-147 ◽  
Author(s):  
Meng Ding ◽  
Ting-Zhu Huang ◽  
Si Wang ◽  
Jin-Jin Mei ◽  
Xi-Le Zhao
Keyword(s):  
Total Variation ◽  
Group Sparsity ◽  
Overlapping Group Sparsity

scholarly journals An Adaptive Image Denoising Model Based on Tikhonov and TV Regularizations

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Kui Liu ◽  
Jieqing Tan ◽  
Benyue Su
Keyword(s):  
Image Denoising ◽  
Total Variation ◽  
Tikhonov Regularization ◽  
Experimental Results ◽  
Total Variation Regularization ◽  
Split Bregman Method ◽  
Split Bregman ◽  
Gradient Information ◽  
Weighted Combination ◽  
Staircase Artifacts

To avoid the staircase artifacts, an adaptive image denoising model is proposed by the weighted combination of Tikhonov regularization and total variation regularization. In our model, Tikhonov regularization and total variation regularization can be adaptively selected based on the gradient information of the image. When the pixels belong to the smooth regions, Tikhonov regularization is adopted, which can eliminate the staircase artifacts. When the pixels locate at the edges, total variation regularization is selected, which can preserve the edges. We employ the split Bregman method to solve our model. Experimental results demonstrate that our model can obtain better performance than those of other models.

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