3-D Total Generalized Variation Method for Dynamic Cardiac MR Image Denoising

2015 ◽  
Vol 8 (3) ◽  
pp. 557-564
Author(s):  
Mingfeng Jiang
Keyword(s):  
Image Denoising ◽  
Variation Method ◽  
Mr Image ◽  
Total Generalized Variation ◽  
Cardiac Mr ◽  
Generalized Variation

scholarly journals Total Generalized Variation Method for Deconvolution-based CT Brain Perfusion

2019 ◽  
Author(s):  
Дмитрий Люков ◽  
Dmitry Lyukov ◽  
Андрей Крылов ◽  
Andrey Krylov ◽  
Василий Лукшин ◽  
...  
Keyword(s):  
Synthetic Data ◽  
Blood Perfusion ◽  
Brain Perfusion ◽  
Variation Method ◽  
Total Generalized Variation ◽  
Perfusion Computed Tomography ◽  
Singular Value Decomposition Method ◽  
Ct Brain ◽  
Generalized Variation ◽  
Value Decomposition

Deconvolution-based method for image analysis of cerebral blood perfusion computed tomography has been suggested. This analysis is the important part of diagnostics of ischemic stroke. The method is based on total generalized variation regularization algorithm. The algorithm was tested with generated synthetic data and clinical data. Proposed algorithm was compared with singular value decomposition method using Tikhonov regularization and with total variation based deconvolution method. It was shown that the suggested algorithm gives better results than these methods. The proposed algorithm combines both deconvolution and denoising processes, so results are more noisy resistant. It can allow to use lower radiation dose.

scholarly journals A wavelet frame constrained total generalized variation model for imaging conductivity distribution

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Yanyan Shi ◽  
Zhiwei Tian ◽  
Meng Wang ◽  
Xiaolong Kong ◽  
Lei Li ◽  
...  
Keyword(s):  
Regularization Method ◽  
Variation Method ◽  
Total Variation Regularization ◽  
Wavelet Frame ◽  
Impedance Tomography ◽  
Conductivity Distribution ◽  
Total Generalized Variation ◽  
Staircase Effect ◽  
Ill Posed ◽  
Generalized Variation

<p style='text-indent:20px;'>Electrical impedance tomography (EIT) is a sensing technique with which conductivity distribution can be reconstructed. It should be mentioned that the reconstruction is a highly ill-posed inverse problem. Currently, the regularization method has been an effective approach to deal with this problem. Especially, total variation regularization method is advantageous over Tikhonov method as the edge information can be well preserved. Nevertheless, the reconstructed image shows severe staircase effect. In this work, to enhance the quality of reconstruction, a novel hybrid regularization model which combines a total generalized variation method with a wavelet frame approach (TGV-WF) is proposed. An efficient mean doubly augmented Lagrangian algorithm has been developed to solve the TGV-WF model. To demonstrate the effectiveness of the proposed method, numerical simulation and experimental validation are conducted for imaging conductivity distribution. Furthermore, some comparisons are made with typical regularization methods. From the results, it can be found that the proposed method shows better performance in the reconstruction since the edge of the inclusion can be well preserved and the staircase effect is effectively relieved.</p>

scholarly journals An Adaptive Total Generalized Variation Model with Augmented Lagrangian Method for Image Denoising

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Chuan He ◽  
Changhua Hu ◽  
Xiaogang Yang ◽  
Huafeng He ◽  
Qi Zhang
Keyword(s):  
Image Denoising ◽  
Augmented Lagrangian ◽  
Regularization Parameter ◽  
Augmented Lagrangian Method ◽  
Lagrangian Method ◽  
Qualitative Assessment ◽  
Edge Preservation ◽  
Total Generalized Variation ◽  
Proposed Model ◽  
Generalized Variation

We propose an adaptive total generalized variation (TGV) based model, aiming at achieving a balance between edge preservation and region smoothness for image denoising. The variable splitting (VS) and the classical augmented Lagrangian method (ALM) are used to solve the proposed model. With the proposed adaptive model and ALM, the regularization parameter, which balances the data fidelity and the regularizer, is refreshed with a closed form in each iterate, and the image denoising can be accomplished without manual interference. Numerical results indicate that our method is effective in staircasing effect suppression and holds superiority over some other state-of-the-art methods both in quantitative and in qualitative assessment.

Zero-filling Technique in Fresnel Transfrom Image Reconstruction for MR Image Denoising

PIERS Online ◽  
2005 ◽  
Vol 1 (4) ◽  
pp. 473-477
Author(s):  
Bin-Rong Wu ◽  
Satoshi Ito ◽  
Yoshitsugu Kamimura ◽  
Yoshifumi Yamada
Keyword(s):  
Image Reconstruction ◽  
Image Denoising ◽  
Mr Image

Mumford-Shah Model Based on Weighted Total Generalized Variation

2012 ◽  
Vol 38 (12) ◽  
pp. 1913 ◽  
Author(s):  
Wen-Juan ZHANG ◽  
Xiang-Chu FENG ◽  
Xu-Dong WANG
Keyword(s):  
Total Generalized Variation ◽  
Model Based ◽  
Generalized Variation
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