CFAR Detection Based on Adaptive Tight Frame and Weighted Group-Sparsity Regularization for OTHR

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.

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

Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment ◽

10.1016/j.nima.2021.166048 ◽

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

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

Water Resources Research ◽

10.1002/2014wr016430 ◽

2015 ◽

Vol 51 (10) ◽

pp. 8607-8626 ◽

Cited By ~ 7

Author(s):

Azarang Golmohammadi ◽

Mohammad‐Reza M. Khaninezhad ◽

Behnam Jafarpour

Keyword(s):

Inverse Problems ◽

Subsurface Flow ◽

Group Sparsity ◽

Sparsity Regularization ◽

Ill Posed

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Distributed Optimization of Quadratic Costs with a Group-Sparsity Regularization Via PDMM

2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) ◽

10.23919/apsipa.2018.8659752 ◽

2018 ◽

Author(s):

Kangwei Hu ◽

Danqi Jin ◽

Wen Zhang ◽

Jie Chen

Keyword(s):

Distributed Optimization ◽

Group Sparsity ◽

Sparsity Regularization

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Denoising with weak signal preservation by group-sparsity transform learning

Geophysics ◽

10.1190/geo2019-0063.1 ◽

2019 ◽

Vol 84 (6) ◽

pp. V351-V368 ◽

Cited By ~ 1

Author(s):

Xiaojing Wang ◽

Bihan Wen ◽

Jianwei Ma

Keyword(s):

Seismic Data ◽

Signal To Noise Ratio ◽

Random Noise ◽

Synthetic Data ◽

Tight Frame ◽

Seismic Exploration ◽

Weak Signal ◽

Group Sparsity ◽

Sparsity Pattern ◽

Group Data

Weak signal preservation is critical in the application of seismic data denoising, especially in deep seismic exploration. It is hard to separate those weak signals in seismic data from random noise because it is less compressible or sparsifiable, although they are usually important for seismic data analysis. Conventional sparse coding models exploit the local sparsity through learning a union of basis, but it does not take into account any prior information about the internal correlation of patches. Motivated by an observation that data patches within a group are expected to share the same sparsity pattern in the transform domain, so-called group sparsity, we have developed a novel transform learning with group sparsity (TLGS) method that jointly exploits local sparsity and internal patch self-similarity. Furthermore, for weak signal preservation, we extended the TLGS method and developed the transform learning with external reference. External clean or denoised patches are applied as the anchored references, which are grouped together with similar corrupted patches. They are jointly modeled under a sparse transform, which is adaptively learned. This is achieved by jointly learning a subset of the transform for each group data. Our method achieves better denoising performance than existing denoising methods, in terms of signal-to-noise ratio values and visual preservation of weak signal. Comparisons of experimental results on one synthetic data and three field data using the [Formula: see text]-[Formula: see text] deconvolution method and the data-driven tight frame method are also provided.

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

Applied Sciences ◽

10.3390/app10165583 ◽

2020 ◽

Vol 10 (16) ◽

pp. 5583 ◽

Cited By ~ 1

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.

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