scholarly journals Local and Nonlocal Steering Kernel Weighted Total Variation Model for Image Denoising

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 329 ◽  
Author(s):  
Rui Lai ◽  
Yiguo Mo ◽  
Zesheng Liu ◽  
Juntao Guan
Keyword(s):  
Image Denoising ◽  
Total Variation ◽  
Noise Suppression ◽  
Nonlocal Symmetry ◽  
Visual Appearance ◽  
Quantitative Indices ◽  
Proposed Model ◽  
Total Variation Model ◽  
Weighted Total Variation ◽  
Tv Model

To eliminate heavy noise and retain more scene details, we propose a structure-oriented total variation (TV) model based on data dependent kernel function and TV criterion for image denoising application. The innovative model introduces the weights produced from the local and nonlocal symmetry features involved in the image itself to pick more precise solutions in the TV denoising process. As a result, the proposed local and nonlocal steering kernel weighted TV model yields excellent noise suppression and structure-preserving performance. The experimental results verify the validity of the proposed model in objective quantitative indices and subjective visual appearance.

scholarly journals Image Denoising Using Total Variation Model Guided by Steerable Filter

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Wenxue Zhang ◽  
Yongzhen Cao ◽  
Rongxin Zhang ◽  
Yuanquan Wang
Keyword(s):  
Diffusion Process ◽  
Total Variation ◽  
Noise Removal ◽  
Edge Preserving ◽  
Linear Diffusion ◽  
Proposed Model ◽  
Total Variation Model ◽  
Adaptive Total Variation ◽  
Steerable Filter ◽  
Tv Model

We propose an adaptive total variation (TV) model by introducing the steerable filter into the TV-based diffusion process for image filtering. The local energy measured by the steerable filter can effectively characterize the object edges and ramp regions and guide the TV-based diffusion process so that the new model behaves like the TV model at edges and leads to linear diffusion in flat and ramp regions. This way, the proposed model can provide a better image processing tool which enables noise removal, edge-preserving, and staircase suppression.

scholarly journals Combined total variation of first and fractional orders for Poisson noise removal in digital images

2021 ◽  
pp. 10-19
Author(s):  
Cong Pham ◽  
Thi Thu Tran ◽  
Minh Pham ◽  
Thanh Cong Nguyen
Keyword(s):  
Image Reconstruction ◽  
Image Restoration ◽  
Total Variation ◽  
Fractional Order ◽  
Optimization Problem ◽  
Poisson Noise ◽  
Experimental Results ◽  
First Order ◽  
Proposed Model ◽  
Total Variation Model

Introduction: Many methods have been proposed to handle the image restoration problem with Poisson noise. A popular approach to Poissonian image reconstruction is the one based on Total Variation. This method can provide significantly sharp edges and visually fine images, but it results in piecewise-constant regions in the resulting images. Purpose: Developing an adaptive total variation-based model for the reconstruction of images contaminated by Poisson noise, and an algorithm for solving the optimization problem. Results: We proposed an effective way to restore images degraded by Poisson noise. Using the Bayesian framework, we proposed an adaptive model based on a combination of first-order total variation and fractional order total variation. The first-order total variation model is efficient for suppressing the noise and preserving the keen edges simultaneously. However, the first-order total variation method usually causes artifact problems in the obtained results. To avoid this drawback, we can use high-order total variation models, one of which is the fractional-order total variation-based model for image restoration. In the fractional-order total variation model, the derivatives have an order greater than or equal to one. It leads to the convenience of computation with a compact discrete form. However, methods based on the fractional-order total variation may cause image blurring. Thus, the proposed model incorporates the advantages of two total variation regularization models, having a significant effect on the edge-preserving image restoration. In order to solve the considered optimization problem, the Split Bregman method is used. Experimental results are provided, demonstrating the effectiveness of the proposed method.  Practical relevance: The proposed method allows you to restore Poissonian images preserving their edges. The presented numerical simulation demonstrates the competitive performance of the model proposed for image reconstruction. Discussion: From the experimental results, we can see that the proposed algorithm is effective in suppressing noise and preserving the image edges. However, the weighted parameters in the proposed model were not automatically selected at each iteration of the proposed algorithm. This requires additional research.

Image Denoising Based on Seperable Total Variation Model

2014 ◽  
Vol 43 (9) ◽  
pp. 910003
Author(s):  
胡辽林 HU Liao-lin ◽  
王斌 WANG Bin ◽  
薛瑞洋 XUE Rui-yang ◽  
王亚萍 WANG Ya-ping
Keyword(s):  
Image Denoising ◽  
Total Variation ◽  
Total Variation Model ◽  
Variation Model

scholarly journals Total Variation Regularization of Matrix-Valued Images

2007 ◽  
Vol 2007 ◽  
pp. 1-11 ◽  
Author(s):  
Oddvar Christiansen ◽  
Tin-Man Lee ◽  
Johan Lie ◽  
Usha Sinha ◽  
Tony F. Chan
Keyword(s):  
Total Variation ◽  
Diffusion Tensor ◽  
Natural Extension ◽  
Positive Definiteness ◽  
Total Variation Regularization ◽  
Diffusion Matrix ◽  
Proposed Model ◽  
Total Variation Model ◽  
Diffusion Tensor Images ◽  
Restoration Model

We generalize the total variation restoration model, introduced by Rudin, Osher, and Fatemi in 1992, to matrix-valued data, in particular, to diffusion tensor images (DTIs). Our model is a natural extension of the color total variation model proposed by Blomgren and Chan in 1998. We treat the diffusion matrixDimplicitly as the productD=LLT, and work with the elements ofLas variables, instead of working directly on the elements ofD. This ensures positive definiteness of the tensor during the regularization flow, which is essential when regularizing DTI. We perform numerical experiments on both synthetical data and 3D human brain DTI, and measure the quantitative behavior of the proposed model.

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