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Keywords = total Lp-norm optimization

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16 pages, 6029 KB  
Article
Infrared Image Deblurring Based on Lp-Pseudo-Norm and High-Order Overlapping Group Sparsity Regularization
by Zhen Ye, Xiaoming Ou, Juhua Huang and Yingpin Chen
Algorithms 2022, 15(9), 327; https://doi.org/10.3390/a15090327 - 14 Sep 2022
Cited by 2 | Viewed by 2316
Abstract
A traditional total variation (TV) model for infrared image deblurring amid salt-and-pepper noise produces a severe staircase effect. A TV model with low-order overlapping group sparsity (LOGS) suppresses this effect; however, it considers only the prior information of the low-order gradient of the [...] Read more.
A traditional total variation (TV) model for infrared image deblurring amid salt-and-pepper noise produces a severe staircase effect. A TV model with low-order overlapping group sparsity (LOGS) suppresses this effect; however, it considers only the prior information of the low-order gradient of the image. This study proposes an image-deblurring model (Lp_HOGS) based on the LOGS model to mine the high-order prior information of an infrared (IR) image amid salt-and-pepper noise. An Lp-pseudo-norm was used to model the salt-and-pepper noise and obtain a more accurate noise model. Simultaneously, the second-order total variation regular term with overlapping group sparsity was introduced into the proposed model to further mine the high-order prior information of the image and preserve the additional image details. The proposed model uses the alternating direction method of multipliers to solve the problem and obtains the optimal solution of the overall model by solving the optimal solution of several simple decoupled subproblems. Experimental results show that the model has better subjective and objective performance than Lp_LOGS and other advanced models, especially when eliminating motion blur. Full article
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20 pages, 473 KB  
Article
A Total Lp-Norm Optimization for Bearing-Only Source Localization in Impulsive Noise with SαS Distribution
by Ji-An Luo, Chang-Cheng Xue, Ying-Jiao Rong and Shen-Tu Han
Sensors 2021, 21(19), 6471; https://doi.org/10.3390/s21196471 - 28 Sep 2021
Cited by 4 | Viewed by 2624
Abstract
This paper considers the problem of robust bearing-only source localization in impulsive noise with symmetric α-stable distribution based on the Lp-norm minimization criterion. The existing Iteratively Reweighted Pseudolinear Least-Squares (IRPLS) method can be used to solve the least LP-norm optimization problem. However, [...] Read more.
This paper considers the problem of robust bearing-only source localization in impulsive noise with symmetric α-stable distribution based on the Lp-norm minimization criterion. The existing Iteratively Reweighted Pseudolinear Least-Squares (IRPLS) method can be used to solve the least LP-norm optimization problem. However, the IRPLS algorithm cannot reduce the bias attributed to the correlation between system matrices and noise vectors. To reduce this kind of bias, a Total Lp-norm Optimization (TLPO) method is proposed by minimizing the errors in all elements of system matrix and data vector based on the minimum dispersion criterion. Subsequently, an equivalent form of TLPO is obtained, and two algorithms are developed to solve the TLPO problem by using Iterative Generalized Eigenvalue Decomposition (IGED) and Generalized Lagrange Multiplier (GLM), respectively. Numerical examples demonstrate the performance advantage of the IGED and GLM algorithms over the IRPLS algorithm. Full article
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18 pages, 411 KB  
Article
Adaptive Redundant Lifting Wavelet Transform Based on Fitting for Fault Feature Extraction of Roller Bearings
by Zijing Yang, Ligang Cai, Lixin Gao and Huaqing Wang
Sensors 2012, 12(4), 4381-4398; https://doi.org/10.3390/s120404381 - 29 Mar 2012
Cited by 19 | Viewed by 7943
Abstract
A least square method based on data fitting is proposed to construct a new lifting wavelet, together with the nonlinear idea and redundant algorithm, the adaptive redundant lifting transform based on fitting is firstly stated in this paper. By variable combination selections of [...] Read more.
A least square method based on data fitting is proposed to construct a new lifting wavelet, together with the nonlinear idea and redundant algorithm, the adaptive redundant lifting transform based on fitting is firstly stated in this paper. By variable combination selections of basis function, sample number and dimension of basis function, a total of nine wavelets with different characteristics are constructed, which are respectively adopted to perform redundant lifting wavelet transforms on low-frequency approximate signals at each layer. Then the normalized lP norms of the new node-signal obtained through decomposition are calculated to adaptively determine the optimal wavelet for the decomposed approximate signal. Next, the original signal is taken for subsection power spectrum analysis to choose the node-signal for single branch reconstruction and demodulation. Experiment signals and engineering signals are respectively used to verify the above method and the results show that bearing faults can be diagnosed more effectively by the method presented here than by both spectrum analysis and demodulation analysis. Meanwhile, compared with the symmetrical wavelets constructed with Lagrange interpolation algorithm, the asymmetrical wavelets constructed based on data fitting are more suitable in feature extraction of fault signal of roller bearings. Full article
(This article belongs to the Section Physical Sensors)
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