The Split Bregman method of image decomposition model for ultrasound image denoising

2010 ◽  
Author(s):  
Guodong Wang ◽  
Zhenkuan Pan ◽  
Zengfang Zhao ◽  
Xiaotong Sun
Keyword(s):  
Image Denoising ◽  
Ultrasound Image ◽  
Image Decomposition ◽  
Split Bregman Method ◽  
Split Bregman ◽  
Decomposition Model

scholarly journals Fast Algorithms for the Anisotropic LLT Model in Image Denoising

2011 ◽  
Vol 1 (3) ◽  
pp. 264-283 ◽  
Author(s):  
Zhi-Feng Pang ◽  
Li-Lian Wang ◽  
Yu-Fei Yang
Keyword(s):  
Convergence Rate ◽  
Image Denoising ◽  
Projection Method ◽  
Split Bregman Method ◽  
Split Bregman ◽  
Minimization Problems ◽  
Iterative Shrinkage ◽  
Speed Up ◽  
Thresholding Algorithm ◽  
Iterative Shrinkage Thresholding

AbstractIn this paper, we propose a new projection method for solving a general minimization problems with twoL1-regularization terms for image denoising. It is related to the split Bregman method, but it avoids solving PDEs in the iteration. We employ the fast iterative shrinkage-thresholding algorithm (FISTA) to speed up the proposed method to a convergence rateO(k−2). We also show the convergence of the algorithms. Finally, we apply the methods to the anisotropic Lysaker, Lundervold and Tai (LLT) model and demonstrate their efficiency.

Split Bregman method for the modified lot model in image denoising

2011 ◽  
Vol 217 (12) ◽  
pp. 5392-5403 ◽  
Author(s):  
Yu-Fei Yang ◽  
Zhi-Feng Pang ◽  
Bao-Li Shi ◽  
Zhi-Guo Wang
Keyword(s):  
Image Denoising ◽  
Split Bregman Method ◽  
Split Bregman

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 An efficient algorithm for adaptive total variation based image decomposition and restoration

2014 ◽  
Vol 24 (2) ◽  
pp. 405-415 ◽  
Author(s):  
Xinwu Liu ◽  
Lihong Huang
Keyword(s):  
Total Variation ◽  
Image Decomposition ◽  
Energy Functional ◽  
Total Variation Regularization ◽  
Split Bregman Method ◽  
Split Bregman ◽  
Edge Preserving ◽  
Sharp Edges ◽  
Adaptive Total Variation ◽  
Important Structure

Abstract With the aim to better preserve sharp edges and important structure features in the recovered image, this article researches an improved adaptive total variation regularization and H−1 norm fidelity based strategy for image decomposition and restoration. Computationally, for minimizing the proposed energy functional, we investigate an efficient numerical algorithm—the split Bregman method, and briefly prove its convergence. In addition, comparisons are also made with the classical OSV (Osher–Sole–Vese) model (Osher et al., 2003) and the TV-Gabor model (Aujol et al., 2006), in terms of the edge-preserving capability and the recovered results. Numerical experiments markedly demonstrate that our novel scheme yields significantly better outcomes in image decomposition and denoising than the existing models.

scholarly journals Unsupervised Texture Segmentation Using Active Contour Model and Oscillating Information

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Guodong Wang ◽  
Zhenkuan Pan ◽  
Qian Dong ◽  
Ximei Zhao ◽  
Zhimei Zhang ◽  
...  
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Image Decomposition ◽  
Smooth Approximation ◽  
Split Bregman ◽  
Piecewise Constant ◽  
Decomposition Model ◽  
Contour Model ◽  
Proposed Model ◽  
Data Term

Textures often occur in real-world images and may cause considerable difficulties in image segmentation. In order to segment texture images, we propose a new segmentation model that combines image decomposition model and active contour model. The former model is capable of decomposing structural and oscillating components separately from texture image, and the latter model can be used to provide smooth segmentation contour. In detail, we just replace the data term of piecewise constant/smooth approximation in CCV (convex Chan-Vese) model with that of image decomposition model-VO (Vese-Osher). Therefore, our proposed model can estimate both structural and oscillating components of texture images as well as segment textures simultaneously. In addition, we design fast Split-Bregman algorithm for our proposed model. Finally, the performance of our method is demonstrated by segmenting some synthetic and real texture images.

Improved anisotropic diffusion ultrasound image denoising method based on logarithmic compression

2013 ◽  
Vol 32 (11) ◽  
pp. 3218-3220
Author(s):  
Jin YANG ◽  
Zhi-qin LIU ◽  
Yao-bin WANG ◽  
Xiao-ming GAO
Keyword(s):  
Image Denoising ◽  
Anisotropic Diffusion ◽  
Ultrasound Image ◽  
Denoising Method
Sign in / Sign up

Export Citation Format

Share Document