Adaptive iterative reconstruction based on relative total variation for low-intensity computed tomography

2019 ◽  
Vol 165 ◽  
pp. 149-162 ◽  
Author(s):  
Changcheng Gong ◽  
Li Zeng
Keyword(s):  
Computed Tomography ◽  
Total Variation ◽  
Iterative Reconstruction ◽  
Low Intensity ◽  
Relative Total Variation

Multi scale detail-preserving denoising method of infrared image via relative total variation

2013 ◽  
Author(s):  
Guang-mang Cui ◽  
Hua-jun Feng ◽  
Zhi-hai Xu ◽  
Qi Li ◽  
Yue-ting Chen
Keyword(s):  
Total Variation ◽  
Infrared Image ◽  
Denoising Method ◽  
Multi Scale ◽  
Relative Total Variation

scholarly journals Ant Colony-Based Hyperparameter Optimisation in Total Variation Reconstruction in X-ray Computed Tomography

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 591
Author(s):  
Manasavee Lohvithee ◽  
Wenjuan Sun ◽  
Stephane Chretien ◽  
Manuchehr Soleimani
Keyword(s):  
Computed Tomography ◽  
Total Variation ◽  
Cross Validation ◽  
Ant Colony ◽  
Reference Image ◽  
X Ray ◽  
X Ray Computed ◽  
Hyperparameter Selection ◽  
The Cross ◽  
Optimal Set

In this paper, a computer-aided training method for hyperparameter selection of limited data X-ray computed tomography (XCT) reconstruction was proposed. The proposed method employed the ant colony optimisation (ACO) approach to assist in hyperparameter selection for the adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm, which is a total-variation (TV) based regularisation algorithm. During the implementation, there was a colony of artificial ants that swarm through the AwPCSD algorithm. Each ant chose a set of hyperparameters required for its iterative CT reconstruction and the correlation coefficient (CC) score was given for reconstructed images compared to the reference image. A colony of ants in one generation left a pheromone through its chosen path representing a choice of hyperparameters. Higher score means stronger pheromones/probabilities to attract more ants in the next generations. At the end of the implementation, the hyperparameter configuration with the highest score was chosen as an optimal set of hyperparameters. In the experimental results section, the reconstruction using hyperparameters from the proposed method was compared with results from three other cases: the conjugate gradient least square (CGLS), the AwPCSD algorithm using the set of arbitrary hyperparameters and the cross-validation method.The experiments showed that the results from the proposed method were superior to those of the CGLS algorithm and the AwPCSD algorithm using the set of arbitrary hyperparameters. Although the results of the ACO algorithm were slightly inferior to those of the cross-validation method as measured by the quantitative metrics, the ACO algorithm was over 10 times faster than cross—Validation. The optimal set of hyperparameters from the proposed method was also robust against an increase of noise in the data and can be applicable to different imaging samples with similar context. The ACO approach in the proposed method was able to identify optimal values of hyperparameters for a dataset and, as a result, produced a good quality reconstructed image from limited number of projection data. The proposed method in this work successfully solves a problem of hyperparameters selection, which is a major challenge in an implementation of TV based reconstruction algorithms.

scholarly journals Relative Total Variation Structure Analysis-Based Fusion Method for Hyperspectral and LiDAR Data Classification

2021 ◽  
Vol 13 (6) ◽  
pp. 1143
Author(s):  
Yinghui Quan ◽  
Yingping Tong ◽  
Wei Feng ◽  
Gabriel Dauphin ◽  
Wenjiang Huang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Total Variation ◽  
Structure Analysis ◽  
Hyperspectral Image ◽  
Gabor Filter ◽  
Heterogeneous Data ◽  
Lidar Data ◽  
Fusion Method ◽  
Wide Range ◽  
Relative Total Variation

The fusion of the hyperspectral image (HSI) and the light detecting and ranging (LiDAR) data has a wide range of applications. This paper proposes a novel feature fusion method for urban area classification, namely the relative total variation structure analysis (RTVSA), to combine various features derived from HSI and LiDAR data. In the feature extraction stage, a variety of high-performance methods including the extended multi-attribute profile, Gabor filter, and local binary pattern are used to extract the features of the input data. The relative total variation is then applied to remove useless texture information of the processed data. Finally, nonparametric weighted feature extraction is adopted to reduce the dimensions. Random forest and convolutional neural networks are utilized to evaluate the fusion images. Experiments conducted on two urban Houston University datasets (including Houston 2012 and the training portion of Houston 2017) demonstrate that the proposed method can extract the structural correlation from heterogeneous data, withstand a noise well, and improve the land cover classification accuracy.

scholarly journals Physics‐Based Iterative Reconstruction for Dual Source and Flying Focal Spot Computed Tomography

2021 ◽  
Author(s):  
Xiao Wang ◽  
Robert D. MacDougall ◽  
Peng Chen ◽  
Charles A. Bouman ◽  
Simon K. Warfield
Keyword(s):  
Computed Tomography ◽  
Iterative Reconstruction ◽  
Focal Spot ◽  
Dual Source

scholarly journals Performance Evaluation of Dorsal Vein Network of Hand Imaging Using Relative Total Variation-Based Regularization for Smoothing Technique in a Miniaturized Vein Imaging System: A Pilot Study

2021 ◽  
Vol 18 (4) ◽  
pp. 1548 ◽  
Author(s):  
Kyuseok Kim ◽  
Hyun-Woo Jeong ◽  
Youngjin Lee
Keyword(s):  
Image Quality ◽  
Total Variation ◽  
Near Infrared ◽  
Imaging System ◽  
Histogram Equalization ◽  
Evaluation Performance ◽  
Visual Evaluation ◽  
Évaluation Performance ◽  
Nir Imaging ◽  
Relative Total Variation

Vein puncture is commonly used for blood sampling, and accurately locating the blood vessel is an important challenge in the field of diagnostic tests. Imaging systems based on near-infrared (NIR) light are widely used for accurate human vein puncture. In particular, segmentation of a region of interest using the obtained NIR image is an important field, and research for improving the image quality by removing noise and enhancing the image contrast is being widely conducted. In this paper, we propose an effective model in which the relative total variation (RTV) regularization algorithm and contrast-limited adaptive histogram equalization (CLAHE) are combined, whereby some major edge information can be better preserved. In our previous study, we developed a miniaturized NIR imaging system using light with a wavelength of 720–1100 nm. We evaluated the usefulness of the proposed algorithm by applying it to images acquired by the developed NIR imaging system. Compared with the conventional algorithm, when the proposed method was applied to the NIR image, the visual evaluation performance and quantitative evaluation performance were enhanced. In particular, when the proposed algorithm was applied, the coefficient of variation was improved by a factor of 15.77 compared with the basic image. The main advantages of our algorithm are the high noise reduction efficiency, which is beneficial for reducing the amount of undesirable information, and better contrast. In conclusion, the applicability and usefulness of the algorithm combining the RTV approach and CLAHE for NIR images were demonstrated, and the proposed model can achieve a high image quality.

Sparse-view image reconstruction via total absolute curvature combining total variation for X-ray computed tomography

2017 ◽  
Vol 25 (6) ◽  
pp. 959-980 ◽  
Author(s):  
Zhizhong Zheng ◽  
Ailong Cai ◽  
Lei Li ◽  
Bin Yan ◽  
Fulong Le ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Total Variation ◽  
X Ray ◽  
Total Absolute Curvature ◽  
Absolute Curvature ◽  
X Ray Computed
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