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Accelerated parabolic Radon domain 2D adaptive multiple subtraction with fast iterative shrinkage thresholding algorithm and its application in parabolic Radon domain hybrid demultiple method

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JOURNAL OF APPLIED GEOPHYSICS 2017年 143卷 86-102页

作者： Li, Zhong-xiao Li, Zhen-chun China Univ Petr East China Sch Geosci Dept Geophys Qingdao 266580 Shandong Peoples R China Qingdao Natl Lab Marine Sci & Technol Lab Marine Mineral Resources Qingdao 266071 Peoples R China

Adaptive multiple subtraction is an important step for successfully conducting surface-related multiple elimination in marine seismic exploration. 2D adaptive multiple subtraction conducted in the parabolic Radon domain has been proposed to better separate primaries and multiples than 2D adaptive multiple subtraction conducted in the time-offset domain. Additionally, the parabolic Radon domain hybrid demultiple method combining parabolic Radon filtering and parabolic Radon domain 2D adaptive multiple subtraction can better remove multiples than the cascaded demultiple method using time-offset domain 2D adaptive multiple subtraction and the parabolic Radon transform method sequentially. To solve the matching filter in the optimization problem with L1 norm minimization constraint of primaries, traditional parabolic Radon domain 2D adaptive multiple subtraction uses the iterative reweighted least squares (IRIS) algorithm, which is computationally expensive for solving a weighted LS inversion in each iteration. In this paper we introduce the fast iterative shrinkage thresholding algorithm (FISTA) as a faster alternative to the IRIS algorithm for parabolic Radon domain 2D adaptive multiple subtraction. FISTA uses the shrinkage-thresholding operator to promote the sparsity of estimated primaries and solves the 2D matching filter with iterative steps. FISTA based parabolic Radon domain 2D adaptive multiple subtraction reduces the computation time effectively while achieving similar accuracy compared with IRLS based parabolic Radon domain 2D adaptive multiple subtraction. Additionally, the provided examples show that FISTA based parabolic Radon domain 2D adaptive multiple subtraction can better separate primaries and multiples than FISTA based time-offset domain 2D adaptive multiple subtraction, Furthermore, we introduce FISTA based parabolic Radon domain 2D adaptive multiple subtraction into the parabolic Radon domain hybrid demultiple method to improve its computation eff

关键词： 2D adaptive multiple subtraction Parabolic Radon domain Accelerated fast iterative shrinkage thresholding algorithm

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Accelerated 3D blind separation of convolved mixtures based on the fast iterative shrinkage thresholding algorithm for adaptive multiple subtraction

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GEOPHYSICS 2018年第2期83卷 V99-V113页

作者： Li, Zhong-Xiao Li, Zhen-Chun Qingdao Univ Sch Elect Informat Dept Elect Engn Qingdao Peoples R China China Univ Petr East China Sch Geosci Dept Geophys Qingdao Peoples R China Qingdao Natl Lab Marine Sci & Technol Lab Marine Mineral Resources Qingdao Peoples R China

After multiple prediction, adaptive multiple subtraction is essential for the success of multiple removal. The 3D blind separation of convolved mixtures (3D BSCM) method, which is effective in conducting adaptive multiple subtraction, needs to solve an optimization problem containing L1-norm minimization constraints on primaries by the iterative reweighted least-squares (IRLS) algorithm. The 3D BSCM method can better separate primaries and multiples than the 1D/2D BSCM method and the method with energy minimization constraints on primaries. However, the 3D BSCM method has high computational cost because the IRLS algorithm achieves nonquadratic optimization with an LS optimization problem solved in each iteration. In general, it is good to have a faster 3D BSCM method. To improve the adaptability of field data processing, the fast iterative shrinkage thresholding algorithm (FISTA) is introduced into the 3D BSCM method. The proximity operator of FISTA can solve the L1-norm minimization problem efficiently. We demonstrate that our FISTA-based 3D BSCM method achieves similar accuracy of estimating primaries as that of the reference IRLS-based 3D BSCM method. Furthermore, our FISTA-based 3D BSCM method reduces computation time by approximately 60% compared with the reference IRLS-based 3D BSCM method in the synthetic and field data examples.

关键词： 3D blind separation of convolved mixtures fast iterative shrinkage thresholding algorithm L1-norm minimization constraint adaptive multiple subtraction

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Multichannel predictive deconvolution based on the fast iterative shrinkage-thresholding algorithm

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GEOPHYSICS 2016年第1期81卷 V17-V30页

作者： Li, Zhong-Xiao Li, Zhen-Chun Lu, Wen-Kai China Univ Petr East China Sch Geosci Dept Geophys Qingdao Peoples R China Tsinghua Univ Tsinghua Natl Lab Informat Sci & Technol State Key Lab Intelligent Technol & Syst Dept Automat Beijing 100084 Peoples R China

Multichannel predictive deconvolution can accommodate the lateral variations of subsurface structures to some extent and better preserve primaries than can single-channel predictive deconvolution. To solve the 2D predictive filter, traditional multichannel predictive deconvolution uses the least-squares (LS) algorithm, which requires orthogonality between primaries and multiples. In areas where primaries and multiples overlap, traditional LS-based multichannel predictive deconvolution can cause distorted primaries and residual multiples. To avoid the orthogonality assumption required by the LS algorithm, the iterative reweighted LS (IRLS) algorithm and the fast iterative shrinkage-thresholding (FIST) algorithm can be used to solve the prediction filter using the l(1) norm. The FIST algorithm uses the shrinkage-thresholding operator to promote the sparsity of estimated primaries and solves the predictive filter with iterative steps. Compared with the IRLS algorithm, the FIST algorithm can reduce the computation burden effectively while achieving similar accuracy. We have used the FIST algorithm for multichannel predictive deconvolution using the l(1) norm. Compared with traditional FIST-based single-channel predictive deconvolution and LS-based multichannel predictive deconvolution, our method can better balance primary preservation and multiple removal. Tests using synthetic and field data sets proved the effectiveness of the proposed method.

关键词： multichannel predictive deconvolution fast iterative shrinkage thresholding algorithm 2D predictive filter temporal and spatial data window

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Using LASSO for formulating constraint of least-squares programming for solving one-norm equality constrained problem

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SIGNAL IMAGE AND VIDEO PROCESSING 2017年第1期11卷 179-186页

作者： Li, Ya Ling, Bingo Wing-Kuen Xie, Langxiong Dai, Qingyun Guangdong Univ Technol Fac Informat Engn Guangzhou 510006 Guangdong Peoples R China

The paper proposes an efficient method for solving a one-norm equality constrained optimization problem. In fact, this kind of optimization problems is nonconvex. First, the problem is formulated as the least absolute shrinkage and selection operator (LASSO) optimization problem. Then, it is solved by iterative shrinkage algorithms such as the fast iterative shrinkage thresholding algorithm. Next, the solution of the LASSO optimization problem is employed for formulating the constraint of the corresponding least-squares constrained optimization problem. The solution of the least-squares constrained optimization problem is taken as a near globally optimal solution of the one-norm equality constrained optimization problem. The main advantage of this proposed method is that a solution with both lower one-norm constraint error and two-norm reconstruction error can be obtained compared to those of the LASSO problem, while the required computational power is significantly reduced compared to the full search approach. Computer numerical simulation results are illustrated.

关键词： One-norm equality constrained optimization Least absolute shrinkage and selection operator optimization iterative shrinkage algorithm fast iterative shrinkage thresholding algorithm Least-squares constrained optimization Nonconvex optimization

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MRI Image Fusion Based on Optimized Dictionary Learning and Binary Map Refining in Gradient Domain

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第2期82卷 2539-2561页

作者： Hu, Qiu Hu, Shaohai Ma, Xiaole Zhang, Fengzhen Fang, Jing Beijing Jiaotong Univ Inst Informat Sci Beijing 100044 Peoples R China Beijing Key Lab Adv Informat Sci & Network Techno Beijing 100044 Peoples R China Chinese Acad Sci Natl Astron Observ Key Lab Solar Act Beijing 100101 Peoples R China Shandong Normal Univ Sch Phys & Elect Shandong Prov Key Lab Med Phys & Image Proc Techn Jinan 250014 Peoples R China

The insufficient ability of edge feature extraction and high complexity limit the ability of sparse representation to obtain better medical image fusion performance. In this letter, we propose a novel multimodal medical image fusion method with optimized dictionary learning and binary map refining. The optimized dictionary learning uses loop iterations between separable FISTA and manifold-based conjugate gradient algorithm to catch detail texture features in detail layer, and the binary map refining solution adopts Gabor energy measurement with GDGIF to reserve structure and brightness characteristics in base layer. Experimental results of various medical images and clinical applications indicate the effectiveness of the proposed method.

关键词： Clinical application Medical image fusion Dictionary learning fast iterative shrinkage thresholding algorithm Gradient domain guided image filtering

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Modelling short-term variations of differential code bias aiding in extraction of ionospheric observables with sparse kernel learning

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ADVANCES IN SPACE RESEARCH 2022年第7期69卷 2836-2851页

作者： Xu, Lei Li, Zengke Gao, Jingxiang Yang, Xu She, Wenwen China Univ Min & Technol Key Lab Land Environm & Disaster Monitoring Minis MNR Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Sch Environm Sci & Spatial Informat Xuzhou 221116 Jiangsu Peoples R China Anhui Univ Sci & Technol Sch Spatial Informat & Geomat Engn Huainan 232001 Peoples R China Jiangsu Qual Supervis & Inspect Stn Surveying Pro Nanjing 210013 Peoples R China

As short-term variations existing in differential code bias of receivers (RDCB) will decrease the accuracies of extracting ionospheric observables by carrier-to-code leveling (CCL) method, a modified carrier-to code leveling (MCCL) method had been proposed to estimate RDCB offsets epoch by epoch in retrieval of ionospheric information. However, MCCL method is hampered by efficiency and precision at higher sampling intervals. To address RDCB offsets in higher sampling rate domain, a kernel model in the form of radial basis function (RBF) was chosen to model RDCB offsets produced by MCCL using lower sampling rate data in this paper. Owing to ill-posed problem induced by setting to many kernels, L1 norm regulation was employed to promote the sparsity of the problem and fast iterative shrinkage thresholding algorithm (FISTA) was adopted to find the sparse solution. Data sets of 350 IGS stations evenly distributed in high-solar and low-solar activity years were chosen to illustrate the general existences of RDCB offsets and their dependence on solar activity. To validate the reliability and efficiency of the proposed method, two stations of them were selected out to construct the spares kernel model. As shown in experimental results, kernel model is of good performance in training data with over 90% sparsity rate and all standard deviations of the errors between original and fitting data are around 0.4 ns. It is a striking finding in terms of efficiency that the proposed method and CCL are much more efficient than MCCL in extracting ionospheric observables by means of fixed equipment. For further validations, generalized triangular series function (GTSF) was constructed using ionospheric observables derived from precise point positioning (PPP), CCL, MCCL and the proposed method. With Center for Orbit Determination in Europe (CODE) Global Ionospheric Map (GIM) regarded as reference, compared with VTEC estimates derived from CCL, those derived from MCCL and the proposed me

关键词： RDCB offset Ionospheric observable Sparse kernel learning fast iterative shrinkage thresholding algorithm

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An improved linear convergence of FISTA for the LASSO problem with application to CT image reconstruction

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JOURNAL OF COMBINATORIAL OPTIMIZATION 2021年第4期42卷 831-847页

作者： Li, Qian Zhang, Wei Shanghai Univ Engn Sci Sch Math Phys & Stat Shanghai 201620 Peoples R China Natl Univ Singapore Ctr Excellence Modelling & Simulat Next Generat P Singapore 119077 Singapore

The LASSO problem has been explored extensively for CT image reconstruction, the most useful algorithm to solve the LASSO problem is the FISTA. In this paper, we prove that FISTA has a better linear convergence rate than ISTA. Besides, we observe that the convergence rate of FISTA is closely related to the acceleration parameters used in the algorithm. Based on this finding, an acceleration parameter setting strategy is proposed. Moreover, we adopt the function restart scheme on FISTA to reconstruct CT images. A series of numerical experiments is carried out to show the superiority of FISTA over ISTA on signal processing and CT image reconstruction. The numerical experiments consistently demonstrate our theoretical results.

关键词： LASSO problem fast iterative shrinkage thresholding algorithm Linear convergence CT image reconstruction

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