High order total variation method for interior tomography

2012 ◽  
Author(s):  
Jiansheng Yang ◽  
Hengyong Yu ◽  
Wenxiang Cong ◽  
Ming Jiang ◽  
Ge Wang
Keyword(s):  
Total Variation ◽  
High Order ◽  
Variation Method

scholarly journals Infrared Image Super-Resolution Reconstruction Based on Quaternion and High-Order Overlapping Group Sparse Total Variation

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5139 ◽  
Author(s):  
Xingguo Liu ◽  
Yingpin Chen ◽  
Zhenming Peng ◽  
Juan Wu
Keyword(s):  
Total Variation ◽  
Super Resolution ◽  
High Order ◽  
Practical Significance ◽  
Variation Method ◽  
Reconstruction Method ◽  
Infrared Images ◽  
Denoising Method ◽  
Image Structure ◽  
Imaging Characteristics

Owing to the limitations of imaging principles and system imaging characteristics, infrared images generally have some shortcomings, such as low resolution, insufficient details, and blurred edges. Therefore, it is of practical significance to improve the quality of infrared images. To make full use of the information on adjacent points, preserve the image structure, and avoid staircase artifacts, this paper proposes a super-resolution reconstruction method for infrared images based on quaternion total variation and high-order overlapping group sparse. The method uses a quaternion total variation method to utilize the correlation between adjacent points to improve image anti-noise ability and reconstruction effect. It uses the sparsity of a higher-order gradient to reconstruct a clear image structure and restore smooth changes. In addition, we performed regularization by using the denoising method, alternating direction method of multipliers, and fast Fourier transform theory to improve the efficiency and robustness of our method. Our experimental results show that this method has excellent performance in objective evaluation and subjective visual effects.

Total variation and high-order total variation adaptive model for restoring blurred images with Cauchy noise

2019 ◽  
Vol 77 (5) ◽  
pp. 1255-1272 ◽  
Author(s):  
Jing-Hua Yang ◽  
Xi-Le Zhao ◽  
Jin-Jin Mei ◽  
Si Wang ◽  
Tian-Hui Ma ◽  
...  
Keyword(s):  
Total Variation ◽  
High Order ◽  
Adaptive Model ◽  
Blurred Images

A Total Variation Method for MR Renography Denoising based on Mathematical Morphology Reconstruction

2012 ◽  
Vol 7 (14) ◽  
pp. 494-502 ◽  
Author(s):  
Chunyan Yu ◽  
Ying Li ◽  
Ailian Liu ◽  
Bingxin Liu ◽  
Peng Chen
Keyword(s):  
Total Variation ◽  
Mathematical Morphology ◽  
Variation Method

scholarly journals Image Restoration by a Mixed High-Order Total Variation and l1 Regularization Model

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Jianguang Zhu ◽  
Kai Li ◽  
Binbin Hao
Keyword(s):  
Total Variation ◽  
High Order ◽  
Variational Model ◽  
Total Variation Regularization ◽  
Current State ◽  
Model Combining ◽  
Alternating Direction ◽  
Proposed Model ◽  
Sharp Edges ◽  
Thresholding Algorithm

Total variation regularization is well-known for recovering sharp edges; however, it usually produces staircase artifacts. In this paper, in order to overcome the shortcoming of total variation regularization, we propose a new variational model combining high-order total variation regularization and l1 regularization. The new model has separable structure which enables us to solve the involved subproblems more efficiently. We propose a fast alternating method by employing the fast iterative shrinkage-thresholding algorithm (FISTA) and the alternating direction method of multipliers (ADMM). Compared with some current state-of-the-art methods, numerical experiments show that our proposed model can significantly improve the quality of restored images and obtain higher SNR and SSIM values.

Wavelet Total Variation Method Applied to Partial Discharge Signal Denoising

2018 ◽  
Author(s):  
Paulo V. do C. Batista ◽  
Hilton de O. Mota ◽  
Gustavo M. Ferreira ◽  
Fernando T. de A. Silva ◽  
Flavio H. Vasconcelos
Keyword(s):  
Total Variation ◽  
Partial Discharge ◽  
Variation Method ◽  
Signal Denoising
Sign in / Sign up

Export Citation Format

Share Document