An efficient adaptive total variation regularization for image denoising for mobile communication in 5G

2016 ◽  
Author(s):  
Haiqi Jiang
Keyword(s):  
Image Denoising ◽  
Total Variation ◽  
Mobile Communication ◽  
Total Variation Regularization ◽  
Adaptive Total Variation

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 Optoacoustic model-based inversion using anisotropic adaptive total-variation regularization

2019 ◽  
Vol 16 ◽  
pp. 100142
Author(s):  
Shai Biton ◽  
Nadav Arbel ◽  
Gilad Drozdov ◽  
Guy Gilboa ◽  
Amir Rosenthal
Keyword(s):  
Total Variation ◽  
Total Variation Regularization ◽  
Model Based ◽  
Adaptive Total Variation

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.

scholarly journals Adaptive Total Variation Minimization-Based Image Enhancement from Flash and No-Flash Pairs

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Sang Min Yoon ◽  
Yeon Ju Lee ◽  
Gang-Joon Yoon ◽  
Jungho Yoon
Keyword(s):  
Image Enhancement ◽  
Image Denoising ◽  
Total Variation ◽  
Main Idea ◽  
Numerical Results ◽  
Ambient Light ◽  
Total Variation Minimization ◽  
Novel Approach ◽  
Adaptive Total Variation

We present a novel approach for enhancing the quality of an image captured from a pair of flash and no-flash images. The main idea for image enhancement is to generate a new image by combining the ambient light of the no-flash image and the details of the flash image. In this approach, we propose a method based on Adaptive Total Variation Minimization (ATVM) so that it has an efficient image denoising effect by preserving strong gradients of the flash image. Some numerical results are presented to demonstrate the effectiveness of the proposed scheme.

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