Promote quantitative ischemia imaging via myocardial perfusion CT iterative reconstruction with tensor total generalized variation regularization

2018 ◽  
Vol 63 (12) ◽  
pp. 125009 ◽  
Author(s):  
Chengwei Gu ◽  
Dong Zeng ◽  
Jiahui Lin ◽  
Sui Li ◽  
Ji He ◽  
...  
Keyword(s):  
Myocardial Perfusion ◽  
Iterative Reconstruction ◽  
Perfusion Ct ◽  
Total Generalized Variation ◽  
Generalized Variation

scholarly journals Iterative reconstruction for photon-counting CT using prior image constrained total generalized variation

2018 ◽  
Vol 103 ◽  
pp. 167-182 ◽  
Author(s):  
Shanzhou Niu ◽  
You Zhang ◽  
Yuncheng Zhong ◽  
Guoliang Liu ◽  
Shaohui Lu ◽  
...  
Keyword(s):  
Iterative Reconstruction ◽  
Photon Counting ◽  
Total Generalized Variation ◽  
Generalized Variation

Iterative reconstruction for sparse-view X-ray CT using alpha-divergence constrained total generalized variation minimization

2017 ◽  
Vol 25 (4) ◽  
pp. 673-688 ◽  
Author(s):  
Shanzhou Niu ◽  
Jing Huang ◽  
Zhaoying Bian ◽  
Dong Zeng ◽  
Wufan Chen ◽  
...  
Keyword(s):  
Iterative Reconstruction ◽  
Total Generalized Variation ◽  
X Ray ◽  
Generalized Variation

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

Multiband image fusion using total generalized variation regularization

2021 ◽  
Author(s):  
Jun Liang ◽  
Han Pan ◽  
Ying Ya ◽  
Zhongliang Jing ◽  
Lingfeng Qiao
Keyword(s):  
Image Fusion ◽  
Total Generalized Variation ◽  
Generalized Variation

scholarly journals Second-order total generalized variation based model for restoring images with mixed Poisson — Gaussian noise

2021 ◽  
pp. 20-32
Author(s):  
Cong Pham ◽  
Thi Thu Thao Tran ◽  
Thanh Cong Nguyen ◽  
Duc Hoang Vo
Keyword(s):  
Gaussian Noise ◽  
High Efficiency ◽  
Gaussian Model ◽  
Second Order ◽  
Total Generalized Variation ◽  
Proposed Model ◽  
Total Variation Model ◽  
Alternating Minimization Algorithm ◽  
Generalized Variation ◽  
Noise Models

Introduction: A common problem in image restoration is image denoising. Among many noise models, the mixed Poisson-Gaussian model has recently aroused considerable interest. Purpose: Development of a model for denoising images corrupted by mixed Poisson-Gaussian noise, along with an algorithm for solving the resulting minimization problem. Results: We proposed a new total variation model for restoring an image with mixed Poisson-Gaussian noise, based on second-order total generalized variation. In order to solve this problem, an efficient alternating minimization algorithm is used. To illustrate its comparison with related methods, experimental results are presented, demonstrating the high efficiency of the proposed approach. Practical relevance: The proposed model allows you to remove mixed Poisson-Gaussian noise in digital images, preserving the edges. The presented numerical results demonstrate the competitive features of the proposed model.

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