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学者姓名：陈光永
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Abstract ：
The graph-information-based fuzzy clustering has shown promising results in various datasets. However, its performance is hindered when dealing with high-dimensional data due to challenges related to redundant information and sensitivity to the similarity matrix design. To address these limitations, this article proposes an implicit fuzzy k-means (FKMs) model that enhances graph-based fuzzy clustering for high-dimensional data. Instead of explicitly designing a similarity matrix, our approach leverages the fuzzy partition result obtained from the implicit FKMs model to generate an effective similarity matrix. We employ a projection-based technique to handle redundant information, eliminating the need for specific feature extraction methods. By formulating the fuzzy clustering model solely based on the similarity matrix derived from the membership matrix, we mitigate issues, such as dependence on initial values and random fluctuations in clustering results. This innovative approach significantly improves the competitiveness of graph-enhanced fuzzy clustering for high-dimensional data. We present an efficient iterative optimization algorithm for our model and demonstrate its effectiveness through theoretical analysis and experimental comparisons with other state-of-the-art methods, showcasing its superior performance.
Keyword ：
Computational modeling Computational modeling Data models Data models Feature extraction Feature extraction Fuzzy clustering Fuzzy clustering graph clustering graph clustering high-dimensional data high-dimensional data High-dimensional data High-dimensional data Image segmentation Image segmentation implicit model implicit model Manifolds Manifolds Optimization Optimization
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| GB/T 7714 | Shi, Zhaoyin , Chen, Long , Ding, Weiping et al. IFKMHC: Implicit Fuzzy K-Means Model for High-Dimensional Data Clustering [J]. | IEEE TRANSACTIONS ON CYBERNETICS , 2024 . |
| MLA | Shi, Zhaoyin et al. "IFKMHC: Implicit Fuzzy K-Means Model for High-Dimensional Data Clustering" . | IEEE TRANSACTIONS ON CYBERNETICS (2024) . |
| APA | Shi, Zhaoyin , Chen, Long , Ding, Weiping , Zhong, Xiaopin , Wu, Zongze , Chen, Guang-Yong et al. IFKMHC: Implicit Fuzzy K-Means Model for High-Dimensional Data Clustering . | IEEE TRANSACTIONS ON CYBERNETICS , 2024 . |
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Separable nonlinear models are pervasively employed in diverse disciplines, such as system identification, signal analysis, electrical engineering, and machine learning. Identifying these models inherently poses a non-convex optimization challenge. While gradient descent (GD) is a commonly adopted method, it is often plagued by suboptimal convergence rates and is highly dependent on the appropriate choice of step size. To mitigate these issues, we introduce an augmented GD algorithm enhanced with Anderson acceleration (AA), and propose a Hierarchical GD with Anderson acceleration (H-AAGD) method for efficient identification of separable nonlinear models. This novel approach transcends the conventional step size constraints of GD algorithms and considers the coupling relationships between different parameters during the optimization process, thereby enhancing the efficiency of the solution-finding process. Unlike the Newton method, our algorithm obviates the need for computing the inverse of the Hessian matrix, simplifying the computational process. Additionally, we theoretically analyze the convergence and complexity of the algorithm and validate its effectiveness through a series of numerical experiments.
Keyword ：
Anderson acceleration Anderson acceleration Hierarchical identification algorithm Hierarchical identification algorithm Robust parameter estimation Robust parameter estimation Separable nonlinear problem Separable nonlinear problem
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| GB/T 7714 | Chen, Guang-Yong , Lin, Xin , Xue, Peng et al. Efficient gradient descent algorithm with anderson acceleration for separable nonlinear models [J]. | NONLINEAR DYNAMICS , 2024 . |
| MLA | Chen, Guang-Yong et al. "Efficient gradient descent algorithm with anderson acceleration for separable nonlinear models" . | NONLINEAR DYNAMICS (2024) . |
| APA | Chen, Guang-Yong , Lin, Xin , Xue, Peng , Gan, Min . Efficient gradient descent algorithm with anderson acceleration for separable nonlinear models . | NONLINEAR DYNAMICS , 2024 . |
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Reflection removal is a crucial issue in image reconstruction, especially for high-definition images. Removing undesirable reflections can greatly enhance the performance of various visual systems, such as medical imaging, autonomous driving, and security surveillance. However, the resolution of existing reflection removal datasets is not high and the training data heavily relies on synthetic data, which hampers the performance of reflection removal methods and restricts the development of effective techniques tailored for high-definition images. Therefore, this paper introduces a new dataset, Real-world Reflection Removal in 4K (RR4K). This novel dataset, with its large capacity and high resolution of 6000×4000 pixels, represents a significant advancement in the field, ensuring a realistic and high quality benchmark. Furthermore, building upon the dataset, we propose an efficient method for single-image reflection removal, optimized for high-definition processing. This method employs the U-Net architecture, enhanced with large kernel distillation and scale-aware features, enabling it to effectively handle complex reflection scenarios while reducing computational demands. Comprehensive testing on the RR4K dataset and existing low-resolution datasets has demonstrated the method's superior efficiency and effectiveness. We believe that our constructed RR4K dataset can better evaluate and design algorithms for removing undesirable reflection from real-world high-definition images. Our dataset and code are available at GitHub†. © 1991-2012 IEEE.
Keyword ：
Benchmark Dataset Benchmark Dataset Image Reconstruction Image Reconstruction Single-Image Reflection Removal Single-Image Reflection Removal
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| GB/T 7714 | Chen, G.-Y. , Zheng, C.-W. , Fan, G.-D. et al. Real-World Image Reflection Removal: An Ultra-High-Definition Dataset and an Efficient Baseline [J]. | IEEE Transactions on Circuits and Systems for Video Technology , 2024 . |
| MLA | Chen, G.-Y. et al. "Real-World Image Reflection Removal: An Ultra-High-Definition Dataset and an Efficient Baseline" . | IEEE Transactions on Circuits and Systems for Video Technology (2024) . |
| APA | Chen, G.-Y. , Zheng, C.-W. , Fan, G.-D. , Su, J.-N. , Gan, M. , Philip, Chen, C.L. . Real-World Image Reflection Removal: An Ultra-High-Definition Dataset and an Efficient Baseline . | IEEE Transactions on Circuits and Systems for Video Technology , 2024 . |
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When capturing images under strong light sources at night, intense lens flare artifacts often appear, significantly degrading visual quality and impacting downstream computer vision tasks. Although transformer-based methods have achieved remarkable results in nighttime flare removal, they fail to adequately distinguish between flare and non-flare regions. This unified processing overlooks the unique characteristics of these regions, leading to suboptimal performance and unsatisfactory results in real-world scenarios. To address this critical issue, we propose a novel approach incorporating Location Prior Guidance (LPG) and a specialized flare removal model, LPFSformer. LPG is designed to accurately learn the location of flares within an image and effectively capture the associated glow effects. By employing Location Prior Injection (LPI), our method directs the model's focus towards flare regions through the interaction of frequency and spatial domains. Additionally, to enhance the recovery of high-frequency textures and capture finer local details, we designed a Global Hybrid Feature Compensator (GHFC). GHFC aggregates different expert structures, leveraging the diverse receptive fields and CNN operations of each expert to effectively utilize a broader range of features during the flare removal process. Extensive experiments demonstrate that our LPFSformer achieves state-of-the-art flare removal performance compared to existing methods. Our code and a pre-trained LPFSformer have been uploaded to GitHub for validation. © 1991-2012 IEEE.
Keyword ：
Deep learning Deep learning Low-level Computer Vision Low-level Computer Vision Nighttime Flare Removal Nighttime Flare Removal
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| GB/T 7714 | Chen, G.-Y. , Dong, W. , Fan, G. et al. LPFSformer: Location Prior Guided Frequency and Spatial Interactive Learning for Nighttime Flare Removal [J]. | IEEE Transactions on Circuits and Systems for Video Technology , 2024 . |
| MLA | Chen, G.-Y. et al. "LPFSformer: Location Prior Guided Frequency and Spatial Interactive Learning for Nighttime Flare Removal" . | IEEE Transactions on Circuits and Systems for Video Technology (2024) . |
| APA | Chen, G.-Y. , Dong, W. , Fan, G. , Su, J.-N. , Gan, M. , Philip, Chen, C.L. . LPFSformer: Location Prior Guided Frequency and Spatial Interactive Learning for Nighttime Flare Removal . | IEEE Transactions on Circuits and Systems for Video Technology , 2024 . |
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Non-local attention (NLA) is a powerful technique for capturing long-range feature correlations in deep single-image super-resolution (SR). However, NLA suffers from high computational complexity and memory consumption, as it requires aggregating all non-local feature information for each query response and recalculating the similarity weight distribution for different abstraction levels of features. To address these challenges, we propose a novel learnable collaborative attention (LCoA) that introduces inductive bias into non-local modeling. Our LCoA consists of two components: learnable sparse pattern (LSP) and collaborative attention (CoA). LSP uses the k-means clustering algorithm to dynamically adjust the sparse attention pattern of deep features, which reduces the number of non-local modeling rounds compared with existing sparse solutions. CoA leverages the sparse attention pattern and weights learned by LSP and co-optimizes the similarity matrix across different abstraction levels, which avoids redundant similarity matrix calculations. The experimental results show that our LCoA can reduce the non-local modeling time by about 83% in the inference stage. In addition, we integrate our LCoA into a deep learnable collaborative attention network (LCoAN), which achieves competitive performance in terms of inference time, memory consumption, and reconstruction quality compared with other state-of-the-art SR methods. Our code and pre-trained LCoAN models were uploaded to GitHub (https://github.com/YigangZhao/LCoAN) for validation. © 2024 SPIE and IS&T.
Keyword ：
k-means clustering k-means clustering non-local attention non-local attention self-similarity self-similarity single-image super-resolution single-image super-resolution
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| GB/T 7714 | Zhao, Y. , Zheng, C. , Su, J. et al. Efficient learnable collaborative attention for single-image super-resolution [J]. | Journal of Electronic Imaging , 2024 , 33 (6) . |
| MLA | Zhao, Y. et al. "Efficient learnable collaborative attention for single-image super-resolution" . | Journal of Electronic Imaging 33 . 6 (2024) . |
| APA | Zhao, Y. , Zheng, C. , Su, J. , Chen, G. . Efficient learnable collaborative attention for single-image super-resolution . | Journal of Electronic Imaging , 2024 , 33 (6) . |
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Robust nonlinear regression frequently arises in data analysis that is affected by outliers in various application fields such as system identification, signal processing, and machine learning. However, it is still quite challenge to design an efficient algorithm for such problems due to the nonlinearity and nonsmoothness. Previous researches usually ignore the underlying structure presenting in the such nonlinear regression models, where the variables can be partitioned into a linear part and a nonlinear part. Inspired by the high efficiency of variable projection algorithm for solving separable nonlinear least squares problems, in this paper, we develop a robust variable projection (RoVP) method for the parameter estimation of separable nonlinear regression problem with $L_{1}$ norm loss. The proposed algorithm eliminates the linear parameters by solving a linear programming subproblem, resulting in a reduced problem that only involves nonlinear parameters. More importantly, we derive the Jacobian matrix of the reduced objective function, which tackles the coupling between the linear parameters and nonlinear parameters. Furthermore, we observed an intriguing phenomenon in the landscape of the original separable nonlinear objective function, where some narrow valleys frequently exist. The RoVP strategy can effectively diminish the likelihood of the algorithm getting stuck in these valleys and accelerate its convergence. Numerical experiments confirm the effectiveness and robustness of the proposed algorithm. IEEE
Keyword ：
Autoregressive processes Autoregressive processes Jacobian matrices Jacobian matrices Linear programming Linear programming Optimization Optimization Parameter estimation Parameter estimation Predictive models Predictive models radial basis function network based autoregressive (RBF-AR) model radial basis function network based autoregressive (RBF-AR) model robust parameter estimation robust parameter estimation Signal processing algorithms Signal processing algorithms System identification System identification variable projection (VP) algorithm variable projection (VP) algorithm
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| GB/T 7714 | Chen, G. , Su, X. , Gan, M. et al. Robust variable projection algorithm for the identification of separable nonlinear models [J]. | IEEE Transactions on Automatic Control , 2024 , 69 (9) : 1-8 . |
| MLA | Chen, G. et al. "Robust variable projection algorithm for the identification of separable nonlinear models" . | IEEE Transactions on Automatic Control 69 . 9 (2024) : 1-8 . |
| APA | Chen, G. , Su, X. , Gan, M. , Guo, W. , Chen, C.L.P. . Robust variable projection algorithm for the identification of separable nonlinear models . | IEEE Transactions on Automatic Control , 2024 , 69 (9) , 1-8 . |
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Sparse coding based domain adaptation methods aim to learn a robust transfer classifier by utilizing the knowledge from source domain and the learned new representation of both domains. Most existing works have achieved remarkable results in solving linear domain shift problems, but have poor performance in nonlinear domain shift problems. In this paper, we propose a Transfer kernel sparse coding based on dynamic distribution alignment (TKSC-DDA) approach for cross-domain visual recognition, which incorporates dynamic distributed alignment into kernel sparse coding to learn discriminative and robust sparse representations. Extensive experiment on visual transfer learning tasks demonstrate that our proposed method can significantly out-perform serval state-of-the-art approaches. © 2024 IEEE.
Keyword ：
Alignment Alignment Computer vision Computer vision Image representation Image representation Learning systems Learning systems
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| GB/T 7714 | Huang, Wei , Gan, Min , Chen, Guangyong . Transfer Kernel Sparse Coding Based on Dynamic Distribution Alignment for Image Representation [C] . 2024 : 230-234 . |
| MLA | Huang, Wei et al. "Transfer Kernel Sparse Coding Based on Dynamic Distribution Alignment for Image Representation" . (2024) : 230-234 . |
| APA | Huang, Wei , Gan, Min , Chen, Guangyong . Transfer Kernel Sparse Coding Based on Dynamic Distribution Alignment for Image Representation . (2024) : 230-234 . |
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Underwater image enhancement, especially in color restoration and detail reconstruction, remains a significant challenge. Current models focus on improving accuracy and learning efficiency through neural network design, often neglecting traditional optimization algorithms’ benefits. We propose FAIN-UIE, a novel approach for color and fine-texture recovery in underwater imagery. It leverages insights from the Fast Iterative Shrink-Threshold Algorithm (FISTA) to approximate image degradation, enhancing network fitting speed. FAIN-UIE integrates the residual degradation module (RDM) and momentum calculation module (MC) for gradient descent and momentum simulation, addressing feature fusion losses with the Feature Merge Block (FMB). By integrating multi-scale information and inter-stage pathways, our method effectively maps multi-stage image features, advancing color and fine-texture restoration. Experimental results validate its robust performance, positioning FAIN-UIE as a competitive solution for practical underwater imaging applications. © 2024 Elsevier Inc.
Keyword ：
Deep learning Deep learning FISTA algorithm FISTA algorithm Proximal gradient Proximal gradient Underwater image enhancement Underwater image enhancement
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| GB/T 7714 | Chen, B.-Y. , Su, J.-N. , Chen, G.-Y. et al. FISTA acceleration inspired network design for underwater image enhancement [J]. | Journal of Visual Communication and Image Representation , 2024 , 103 . |
| MLA | Chen, B.-Y. et al. "FISTA acceleration inspired network design for underwater image enhancement" . | Journal of Visual Communication and Image Representation 103 (2024) . |
| APA | Chen, B.-Y. , Su, J.-N. , Chen, G.-Y. , Gan, M. . FISTA acceleration inspired network design for underwater image enhancement . | Journal of Visual Communication and Image Representation , 2024 , 103 . |
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Separable nonlinear models (SNLMs) are of great importance in system modeling, signal processing, and machine learning because of their flexible structure and excellent description of nonlinear behaviors. The online identification of such models is quite challenging, and previous related work usually ignores the special structure where the estimated parameters can be partitioned into a linear and a nonlinear part. In this brief, we propose an efficient first-order recursive algorithm for SNLMs by introducing the variable projection (VP) step. The proposed algorithm utilizes the recursive least-squares method to eliminate the linear parameters, resulting in a reduced function. Then, the stochastic gradient descent (SGD) algorithm is employed to update the parameters of the reduced function. By considering the tight coupling relationship between linear parameters and nonlinear parameters, the proposed first-order VP algorithm is more efficient and robust than the traditional SGD algorithm and alternating optimization algorithm. More importantly, since the proposed algorithm just uses the first-order information, it is easier to apply it to large-scale models. Numerical results on examples of different sizes confirm the effectiveness and efficiency of the proposed algorithm. © 2012 IEEE.
Keyword ：
Couplings Couplings E-learning E-learning Flexible structures Flexible structures Learning algorithms Learning algorithms Learning systems Learning systems Least squares approximations Least squares approximations Machine learning Machine learning Nonlinear systems Nonlinear systems Numerical methods Numerical methods Online systems Online systems Optimization Optimization Parameter estimation Parameter estimation Random processes Random processes Stochastic models Stochastic models Stochastic systems Stochastic systems
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| GB/T 7714 | Chen, Guang-Yong , Gan, Min , Chen, Long et al. Online Identification of Nonlinear Systems With Separable Structure [J]. | IEEE Transactions on Neural Networks and Learning Systems , 2024 , 35 (6) : 8695-8701 . |
| MLA | Chen, Guang-Yong et al. "Online Identification of Nonlinear Systems With Separable Structure" . | IEEE Transactions on Neural Networks and Learning Systems 35 . 6 (2024) : 8695-8701 . |
| APA | Chen, Guang-Yong , Gan, Min , Chen, Long , Chen, C. L. Philip . Online Identification of Nonlinear Systems With Separable Structure . | IEEE Transactions on Neural Networks and Learning Systems , 2024 , 35 (6) , 8695-8701 . |
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Separable nonlinear models are widely used in various fields such as time series analysis, system modeling, and machine learning, due to their flexible structures and ability to capture nonlinear behavior of data. However, identifying the parameters of these models is challenging, especially when sparse models with better interpretability are desired by practitioners. Previous theoretical and practical studies have shown that variable projection (VP) is an efficient method for identifying separable nonlinear models, but these are based on L2 penalty of model parameters, which cannot be directly extended to deal with sparse constraint. Based on the exploration of the structural characteristics of separable models, this paper proposes gradientbased and trust-region-based variable projection algorithms, which mainly solve two key problems: how to eliminate linear parameters under sparse constraint; and how to deal with the coupling relationship between linear and nonlinear parameters in the model. Finally, numerical experiments on synthetic data and real time series data are conducted to verify the effectiveness of the proposed algorithms.
Keyword ：
Non-smooth constraint Non-smooth constraint Separable nonlinear models Separable nonlinear models Variable projection (VP) Variable projection (VP)
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| GB/T 7714 | Xu, Hui-Lang , Chen, Guang-Yong , Cheng, Si-Qing et al. Variable projection algorithms with sparse constraint for separable nonlinear models [J]. | CONTROL THEORY AND TECHNOLOGY , 2024 , 22 (1) : 135-146 . |
| MLA | Xu, Hui-Lang et al. "Variable projection algorithms with sparse constraint for separable nonlinear models" . | CONTROL THEORY AND TECHNOLOGY 22 . 1 (2024) : 135-146 . |
| APA | Xu, Hui-Lang , Chen, Guang-Yong , Cheng, Si-Qing , Gan, Min , Chen, Jing . Variable projection algorithms with sparse constraint for separable nonlinear models . | CONTROL THEORY AND TECHNOLOGY , 2024 , 22 (1) , 135-146 . |
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