Underwater sonar image denoising through nonconvex total variation regularization and generalized Kullback–Leibler fidelity

2021 ◽  
Author(s):  
Wei Tian ◽  
Zhe Chen ◽  
Jie Shen ◽  
Fengchen Huang ◽  
Lizhong Xu
Keyword(s):  
Image Denoising ◽  
Total Variation ◽  
Total Variation Regularization ◽  
Sonar Image

Four-directional fractional-order total variation regularization for image denoising

2017 ◽  
Vol 26 (05) ◽  
pp. 1 ◽  
Author(s):  
Linna Wu ◽  
Yingpin Chen ◽  
Jiaquan Jin ◽  
Hongwei Du ◽  
Bensheng Qiu
Keyword(s):  
Image Denoising ◽  
Total Variation ◽  
Fractional Order ◽  
Total Variation Regularization

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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