A software-based method for eliminating grid artifacts of a crisscrossed grid by mixed-norm and group-sparsity regularization in digital radiography

2021 ◽  
pp. 166048
Author(s):  
Duhee Jeon ◽  
Hyosung Cho ◽  
Hunwoo Lee ◽  
Hyunwoo Lim ◽  
Myeongkyu Park ◽  
...  
Keyword(s):  
Digital Radiography ◽  
Mixed Norm ◽  
Group Sparsity ◽  
Sparsity Regularization

Sparse angle CT reconstruction with weighted dictionary learning algorithm based on adaptive group-sparsity regularization

2021 ◽  
pp. 1-18
Author(s):  
Tiejun Yang ◽  
Lu Tang ◽  
Qi Tang ◽  
Lei Li
Keyword(s):  
Sparse Representation ◽  
Dictionary Learning ◽  
Learning Algorithm ◽  
Basic Unit ◽  
Ct Reconstruction ◽  
Group Sparsity ◽  
Sparsity Regularization ◽  
Smoothing Effects ◽  
Structural Details ◽  
Non Local

OBJECTIVE: In order to solve the blurred structural details and over-smoothing effects in sparse representation dictionary learning reconstruction algorithm, this study aims to test sparse angle CT reconstruction with weighted dictionary learning algorithm based on adaptive Group-Sparsity Regularization (AGSR-SART). METHODS: First, a new similarity measure is defined in which Covariance is introduced into Euclidean distance, Non-local image patches are adaptively divided into groups of different sizes as the basic unit of sparse representation. Second, the weight factor of the regular constraint terms is designed through the residuals represented by the dictionary, so that the algorithm takes different smoothing effects on different regions of the image during the iterative process. The sparse reconstructed image is modified according to the difference between the estimated value and the intermediate image. Last, The SBI (Split Bregman Iteration) iterative algorithm is used to solve the objective function. An abdominal image, a pelvic image and a thoracic image are employed to evaluate performance of the proposed method. RESULTS: In terms of quantitative evaluations, experimental results show that new algorithm yields PSNR of 48.20, the maximum SSIM of 99.06% and the minimum MAE of 0.0028. CONCLUSIONS: This study demonstrates that new algorithm can better preserve structural details in reconstructed CT images. It eliminates the effect of excessive smoothing in sparse angle reconstruction, enhances the sparseness and non-local self-similarity of the image, and thus it is superior to several existing reconstruction algorithms.

scholarly journals Group‐sparsity regularization for ill‐posed subsurface flow inverse problems

2015 ◽  
Vol 51 (10) ◽  
pp. 8607-8626 ◽  
Author(s):  
Azarang Golmohammadi ◽  
Mohammad‐Reza M. Khaninezhad ◽  
Behnam Jafarpour
Keyword(s):  
Inverse Problems ◽  
Subsurface Flow ◽  
Group Sparsity ◽  
Sparsity Regularization ◽  
Ill Posed

scholarly journals Hyperspectral Unmixing with Gaussian Mixture Model and Spatial Group Sparsity

2019 ◽  
Vol 11 (20) ◽  
pp. 2434
Author(s):  
Qiwen Jin ◽  
Yong Ma ◽  
Erting Pan ◽  
Fan Fan ◽  
Jun Huang ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Group Structure ◽  
Gaussian Mixture ◽  
Hyperspectral Data ◽  
Conditional Density ◽  
Mixed Norm ◽  
Group Sparsity ◽  
Hyperspectral Unmixing ◽  
Endmember Variability

In recent years, endmember variability has received much attention in the field of hyperspectral unmixing. To solve the problem caused by the inaccuracy of the endmember signature, the endmembers are usually modeled to assume followed by a statistical distribution. However, those distribution-based methods only use the spectral information alone and do not fully exploit the possible local spatial correlation. When the pixels lie on the inhomogeneous region, the abundances of the neighboring pixels will not share the same prior constraints. Thus, in this paper, to achieve better abundance estimation performance, a method based on the Gaussian mixture model (GMM) and spatial group sparsity constraint is proposed. To fully exploit the group structure, we take the superpixel segmentation (SS) as preprocessing to generate the spatial groups. Then, we use GMM to model the endmember distribution, incorporating the spatial group sparsity as a mixed-norm regularization into the objective function. Finally, under the Bayesian framework, the conditional density function leads to a standard maximum a posteriori (MAP) problem, which can be solved using generalized expectation-maximization (GEM). Experiments on simulated and real hyperspectral data demonstrate that the proposed algorithm has higher unmixing precision compared with other state-of-the-art methods.

scholarly journals Hyperspectral Image Super-Resolution Based on Spatial Group Sparsity Regularization Unmixing

2020 ◽  
Vol 10 (16) ◽  
pp. 5583 ◽  
Author(s):  
Jun Li ◽  
Yuanxi Peng ◽  
Tian Jiang ◽  
Longlong Zhang ◽  
Jian Long
Keyword(s):  
Spatial Resolution ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Super Resolution ◽  
Superior Performance ◽  
High Spectral Resolution ◽  
Data Sets ◽  
Group Sparsity ◽  
Sparsity Regularization ◽  
High Spatial Resolution Image

A hyperspectral image (HSI) contains many narrow spectral channels, thus containing efficient information in the spectral domain. However, high spectral resolution usually leads to lower spatial resolution as a result of the limitations of sensors. Hyperspectral super-resolution aims to fuse a low spatial resolution HSI with a conventional high spatial resolution image, producing an HSI with high resolution in both the spectral and spatial dimensions. In this paper, we propose a spatial group sparsity regularization unmixing-based method for hyperspectral super-resolution. The hyperspectral image (HSI) is pre-clustered using an improved Simple Linear Iterative Clustering (SLIC) superpixel algorithm to make full use of the spatial information. A robust sparse hyperspectral unmixing method is then used to unmix the input images. Then, the endmembers extracted from the HSI and the abundances extracted from the conventional image are fused. This ensures that the method makes full use of the spatial structure and the spectra of the images. The proposed method is compared with several related methods on public HSI data sets. The results demonstrate that the proposed method has superior performance when compared to the existing state-of-the-art.

Super-pixel algorithm and group sparsity regularization method for compressed sensing MR image reconstruction

Optik ◽  
2017 ◽  
Vol 140 ◽  
pp. 392-404 ◽  
Author(s):  
Hongjuan Yu ◽  
Mingfeng Jiang ◽  
Hairong Chen ◽  
Jie Feng ◽  
Yaming Wang ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Regularization Method ◽  
Group Sparsity ◽  
Mr Image ◽  
Sparsity Regularization ◽  
Mr Image Reconstruction
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