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Non-Local and Fully Connected Tensor Network Decomposition for Remote Sensing Image Denoising

高等学校计算数学学报（英文版） 2024年第2期17卷 379-403页

作者： Zhihui Tu Shunda Chen Jian Lu Lin Li Qingtang Jiang Shenzhen Key Laboratory of Advanced Machine Learning and Applications School of Mathematical SciencesShenzhen UniversityShenzhen 518060China Shenzhen Key Laboratory of Advanced Machine Learning and Applications School of Mathematical SciencesShenzhen UniversityShenzhen 518060China National Center for Applied Mathematics Shenzhen(NCAMS) Shenzhen 518055China Pazhou Lab Guangzhou 510320China School of Electronic Engineering Xidian UniversityXi'an 710071China Department of Mathematics and Statistics University of Missouri-St.LouisSt.LouisMO 63121USA

Remote sensing images(RSIs)encompass abundant spatial and spec-tral/temporal information,finding wide applications in various ***,during image acquisition and transmission,RSI often encounter noise interference,which adversely affects the accuracy of subsequent *** address this is-sue,this paper proposes a novel non-local fully connected tensor network(NLFCTN)decomposition algorithm for denoising RSI,aiming to fully exploit their global cor-relation and non-local self-similarity(NSS)***,as a recently de-veloped tensor decomposition technique,exhibits remarkable capability in captur-ing global correlations and minimizing information *** addition,we introduce an efficient algorithm based on proximal alternating minimization(PAM)to effi-ciently solve the model and prove the *** effectiveness of the pro-posed method is validated through denoising experiments on both simulated and real RSI data,employing objective evaluation metrics and subjective visual *** results of the experiment show that the proposed method outperforms other RSI denoising techniques in terms of denoising performance.

关键词： Remote sensing images denoising tensor decomposition non-local self-similarity

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IMPULSE NOISE REMOVAL BY L1 WEIGHTED NUCLEAR NORM MINIMIZATION

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Journal of Computational Mathematics 2023年第6期41卷 1171-1191页

作者： Jian Lu Yuting Ye Yiqiu Dong Xiaoxia Liu Yuru Zou Shenzhen Key Laboratory of Advanced Machine Learning and Applications College of Mathematics and StatisticsShenzhen UniversityShenzhen 518060China Guangdong Key Laboratory of Intelligent Information Processing Pazhou LabGuangzhou 510335China Department of Applied Mathematics and Computer Science Technical University of Denmark2800 Kgs.LyngbyDenmark

In recent years,the nuclear norm minimization(NNM)as a convex relaxation of the rank minimization has attracted great research *** assigning different weights to singular values,the weighted nuclear norm minimization(WNNM)has been utilized in many ***,most of the work on WNNM is combined with the l 2-data-fidelity term,which is under additive Gaussian noise *** this paper,we introduce the L1-WNNM model,which incorporates the l 1-data-fidelity term and the regularization from *** apply the alternating direction method of multipliers(ADMM)to solve the non-convex minimization problem in this *** exploit the low rank prior on the patch matrices extracted based on the image non-local self-similarity and apply the L1-WNNM model on patch matrices to restore the image corrupted by impulse *** results show that our method can effectively remove impulse noise.

关键词： Image denoising Weighted nuclear norm minimization l 1-data-fidelity term Low rank analysis Impulse noise

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Contextual-aware terrain segmentation network for navigable areas with triple aggregation

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Expert Systems with applications 2025年 287卷

作者： Wei Li Muxin Liao Wenbin Zou Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University 518060 Guangdong shenzhen China College of Electronics and Information Engineering Shenzhen University 518060 Guangdong shenzhen China School of Computer Science and Engineering Jiangxi Agricultural University 330045 Jiangxi Nanchang China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University 518060 Guangdong shenzhen China

Robust terrain feature representation is beneficial for accurately identifying navigable areas in autonomous vehicle systems, especially in complex and unstructured wild terrain environments. However, existing methods struggle to achieve stable and precise feature representation due to their inability to capture sufficient terrain features. To address these issues, we propose the Contextual-Aware Terrain Segmentation Network (CTSNet) with triple aggregation to effectively capture terrain features for safe navigable area segmentation. CTSNet comprises three essential modules: the Global Channel Sparsification Module (GCSM), the Fusion-wise Calibration Module (FCM), and the Attention Channel Entropy Module (ACEM). First, GCSM selectively globalizes channels through Top-K sparsification, enabling the aggregation of rich global contextual cues that guide feature fusion. Second, FCM refines multi-scale feature aggregation by addressing inconsistent feature representations across scales. It explores the spatial-semantic relationships between features of different sizes and enhances feature interaction via channel-wise calibration, ensuring more coherent integration of both fine-grained and global information. Finally, ACEM aggregates meaningful channel features through normalization and information entropy, ensuring that only the most relevant features contribute to the final segmentation output. Through triple aggregation in global semantic, multi-scale feature, and channel entropy, CTSNet obtains satisfactory and contextual-aware terrain features. Extensive experiments on multiple wild terrain datasets demonstrate that CTSNet outperforms state-of-the-art methods in identifying navigable areas. The code will be available at https://***/lv881314/CTSNet .

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Deep learning for Spectrum Sensing of Sub-Nyquist Sampled Signals 20

Deep Learning for Spectrum Sensing of Sub-Nyquist Sampled Si...

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20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Li, Shiyao Ji, Hongbing Li, Lin Lu, Jian School of Electronic Engineering Xidian University Xi'an China College of Mathematics and Statistics Shenzhen University Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen China

ISBN: (纸本)9798350356328

Traditional Nyquist rate sampling faces significant challenges as communication technology progresses towards 6G. Multi-coset sampling emerges as a viable solution by reducing the sampling rate. However, this method necessitates signal restoration before processing. In response, an intelligent spectrum management system has been developed that utilizes deep learning to directly extract semantic information from multi-coset sampled signals, eliminating the need for complex data preprocessing. The system integrates multi-band spectrum sensing and modulation recognition, achieving an impressive 99% accuracy in both modulation recognition of single signals and sub-band positioning of multiple signals. © 2024 IEEE.

关键词： Signal modulation

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BP Neural Network-Based Deep Non-negative Matrix Factorization for Image Clustering 16th

BP Neural Network-Based Deep Non-negative Matrix Factorizati...

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16th International Conference on Intelligent Computing, ICIC 2020

作者： Zeng, Qianwen Chen, Wen-Sheng Pan, Binbin College of Mathematics and Statistics Shenzhen University Shenzhen China Guangdong Key Laboratory of Media Security Shenzhen University Shenzhen China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen518060 China

ISBN: (纸本)9783030607951

Deep non-negative matrix factorization (DNMF) is a promising method for non-negativity multi-layer feature extraction. Most of DNMF algorithms are repeatedly to run single-layer NMF to build the hierarchical structure. They have to eliminate the accumulated error via fine-tuning strategy, which is, however, too time-consuming. To deal with the drawbacks of existing DNMF algorithms, this paper proposes a novel deep auto-encoder using back-propagation neural network (BPNN). It can automatically yield a deep non-negative matrix factorization, called BPNN based DNMF (BP-DNMF). The proposed BP-DNMF algorithm is empirically shown to be convergent. Compared with some state of the art DNMF algorithms, experimental results demonstrate that our approach achieves superior clustering performance and has high computing efficiency as well. © 2020, Springer Nature Switzerland AG.

关键词： Non-negative matrix factorization

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Underwater Image Enhancement via Multi-color Space Correction and Fusion 3

Underwater Image Enhancement via Multi-color Space Correctio...

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3rd International Conference on Electronic Information Engineering and Computer, EIECT 2023

作者： Zhou, Chenyu Chen, Bo Zhang, Ying Chen, Wensheng Pan, Binbin Ji, Jing Shenzhen University Shenzhen Key Laboratory of Advanced Machine Learning and Applications College of Mathematics and Statistics Shenzhen518060 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

ISBN: (纸本)9798350357707

Underwater image enhancement, as an important branch of image processing, has attracted the attention of many scholars in recent years. Due to selective scattering and degradation of light in water, images captured underwater are generally of low quality. To improve the accuracy of underwater image details and color, we propose a novel underwater image enhancement model, which is based on a Multi-color space fusion (MSF) strategy and assigns corresponding weights to each color channel for fusion. Qualitative and quantitative comparisons show that the proposed method can effectively remove image blur, achieve color correction, and significantly improve image sharpness. © 2023 IEEE.

关键词： Color

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A Zeroing Neurodynamic Approach for Solving Time-Varying Optimization Problem with Interval-Valued Cost Function 13

A Zeroing Neurodynamic Approach for Solving Time-Varying Opt...

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13th International Conference on Information Science and Technology, ICIST 2023

作者： Li, Haojin Qin, Sitian Jia, Hongyu Feng, Jiqiang Department of Mathematics Harbin Institute of Technology Weihai264209 China Department of Mathematics and Statistics Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China

ISBN: (纸本)9798350313925

Most of the research on uncertainty and imprecision that is widespread in the real world can be abstracted into interval-valued optimization problems whose cost function or constraint function is a closed interval. In this paper, a zeroing neurodynamic (ZND) approach is designed for solving a class of constrained time-varying optimization problems with closed interval-valued cost functions. By virtue of the projection operator and the optimality conditions of the interval-valued optimization problem, the time-varying interval-valued optimization problem is transformed into a nonlinear equation system to be solved. For the reformulated nonlinear equation system, the proposed ZND solver has the ability to track its theoretical solution within pre-defined time. This significantly improves the convergence rate of the ZND solver, making the ZND solver more promising for complex engineering tasks. To the best of our knowledge, it is the first time applying the ZND solver to solve the time-varying interval value optimization problem. Furthermore, the introduction of the projection operator greatly reduces the computational complexity of this ZND solver, saving costs for its hardware implementation. Finally, a numerical example is given to demonstrate the effectiveness of the proposed approach. © 2023 IEEE.

关键词： Cost functions

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On the first negative Hecke eigenvalue of an automorphic representation of GL_(2)(A_(Q))

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Science China Mathematics 2021年第11期64卷 2381-2394页

作者： Yuk-Kam Lau Ming Ho Ng Hengcai Tang Yingnan Wang Department of Mathematics The University of Hong KongHong KongChina Department of Mathematics The Chinese University of Hong KongHong KongChina Institute of Modern Mathematics School of Mathematics and StatisticsHenan UniversityKaifeng 475004China Shenzhen Key Laboratory of Advanced Machine Learning and Applications College of Mathematics and StatisticsShenzhen UniversityShenzhen 518060China

Letπbe a self-dual irreducible cuspidal automorphic representation of GL_(2)(A_(Q))with trivial central *** Hecke eigenvalue λπ(n)is a real multiplicative function in *** show that λπ(n)<0 for some n<<Q^... 详细信息

关键词： automorphic representation Hecke eigenvalue Maass cusp form sign change

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Multi-Label Active learning Driven by Uncertainty and Inconsistency

Multi-Label Active Learning Driven by Uncertainty and Incons...

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Ran Wang Suhe Ye Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen China

Multi-label active learning (MLAL) can help learn a highperformance multi-label classifier based on a smaller training set by selecting and labeling high-quality samples iteratively. This paper proposes a new MLAL algorithm by introducing a new concept called inconsistency cardinality. During each learning iteration, the current classifier can produce temporal decision labels for the candidate samples. Then, supporting decision regarding each label can be derived from the conditional features based on the lower approximations in fuzzy rough set theory. The inconsistency cardinality is defined as the disagreement degree between these two sets of decisions, which will be combined with uncertainty to give a more comprehensive evaluation on unlabeled samples. Correspondingly, a new MLAL strategy is proposed. Experimental comparisons show the feasibility and effectiveness of the proposed algorithm.

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A Novel ECG Signal Classification Algorithm Based on Common and Specific Components Separation 2nd

A Novel ECG Signal Classification Algorithm Based on Common ...

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2nd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020

作者： Huang, Jianfeng Huang, Chao Yang, Lihua Zhang, Qian School of Financial Mathematics and Statistics Guangdong University of Finance Guangzhou China College of Mathematics and Statistics Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen China Guangdong Province Key Laboratory of Computational Science School of Mathematics Sun Yat-sen University Guangzhou China

ISBN: (纸本)9783030598297

Electrocardiography (ECG) signal classification is a challenging task since the characteristics of ECG signals vary significantly for different patients. In this paper, we propose a new method for ECG signal classification based on the separation of common and specific components of a signal. The common components are obtained via Canonical Correlation Analysis (CCA). After removing the common components from the signal, we map the specific components to a lower dimensional feature space for classification. We first establish a basic model in the binary classification setting and then extend it to a more general version. Numerical experiments results on the MIT-BIH Arrhythmia Database are presented and discussed. © 2020, Springer Nature Switzerland AG.

关键词： Electrocardiography

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