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学者姓名：陈光永
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Abstract ：
In recent years, convolutional neural networks have excelled in image Moiré pattern removal, yet their high memory consumption poses challenges for resource-constrained devices. To address this, we propose the lightweight multi-scale network (LMSNet). Designing lightweight multi-scale feature extraction blocks and efficient adaptive channel fusion modules, we extend the receptive field of feature extraction and introduce lightweight convolutional decomposition. LMSNet achieves a balance between parameter numbers and reconstruction performance. Extensive experiments demonstrate that our LMSNet, with 0.77 million parameters, achieves Moiré pattern removal performance comparable to full high definition demoiréing network (FHDe2Net) with 13.57 million parameters. © 2024 SPIE and IS&T.
Keyword ：
Convolution Convolution Convolutional neural networks Convolutional neural networks Distillation Distillation Extraction Extraction Feature extraction Feature extraction Image reconstruction Image reconstruction
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| GB/T 7714 | Li, XuWen , Gan, Min , Su, JianNan et al. Efficient Moiré pattern removal with lightweight multi-scale feature extraction [J]. | Journal of Electronic Imaging , 2024 , 33 (2) . |
| MLA | Li, XuWen et al. "Efficient Moiré pattern removal with lightweight multi-scale feature extraction" . | Journal of Electronic Imaging 33 . 2 (2024) . |
| APA | Li, XuWen , Gan, Min , Su, JianNan , Chen, GuangYong . Efficient Moiré pattern removal with lightweight multi-scale feature extraction . | Journal of Electronic Imaging , 2024 , 33 (2) . |
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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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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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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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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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Abstract ：
Separable nonlinear models (SNMs) serve as potent instruments for system identification, data analysis, and machine learning. However, the online identification of SNMs poses a greater challenge compared to their offline counterparts, primarily due to the dynamic nature of nonlinear systems. Traditional approaches, including the recursive Gauss-Newton and recursive variable projection methods, falter in managing parameter interdependence, culminating in sluggish convergence and suboptimal outcomes. Addressing these limitations, our study introduces a pioneering multi-innovation-based recursive variabl projection (MIRVP) algorithm, which extends the RVP algorithm with a multi-innovation strategy. This strategy enables the algorithm to handle the interaction of the linear and nonlinear parameters more effectively, by using multiple past innovations instead of only the current one, thus improving the identification effect. The proposed algorithm's effectiveness has been validated through tests on synthetic data, real-life industrial control tracking scenarios such as the Box-Jenkins gas furnace data and Glass tube drawing processing, as well as on the training of large-scale neural networks. The results demonstrate that our algorithm outperforms existing methods in terms of convergence speed and robustness
Keyword ：
Multi-innovation method Multi-innovation method Online identification Online identification Recursive Gauss-Newton method Recursive Gauss-Newton method Separable nonlinear modeling Separable nonlinear modeling
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| GB/T 7714 | Su, Jian-Nan , Su, Xiang-Xiang , Chen, Guang-Yong et al. Multi-innovation-based online variable projection algorithm for a class of nonlinear models [J]. | NONLINEAR DYNAMICS , 2024 , 112 (16) : 14107-14122 . |
| MLA | Su, Jian-Nan et al. "Multi-innovation-based online variable projection algorithm for a class of nonlinear models" . | NONLINEAR DYNAMICS 112 . 16 (2024) : 14107-14122 . |
| APA | Su, Jian-Nan , Su, Xiang-Xiang , Chen, Guang-Yong , Gan, Min , Chen, C. L. Philip . Multi-innovation-based online variable projection algorithm for a class of nonlinear models . | NONLINEAR DYNAMICS , 2024 , 112 (16) , 14107-14122 . |
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Blind super-resolution (BlindSR) has recently attracted attention in the field of remote sensing. Due to the lack of paired data, most works assume that the acquired remote sensing images are high-resolution (HR) and use predefined degradation models to synthesize low-resolution (LR) images for training and evaluation. However, these acquired remote sensing images are often degraded by various factors, which still require super-resolution (SR) reconstruction to meet practical needs. Using them as ground-truth (GT) images will limit the model's ability to restore fine details, resulting in blurry and noisy reconstructions. To overcome these limitations, we propose an unsupervised degradation-aware network which transforms natural images into the degraded domain as real-world remote sensing images. It uses natural images containing rich texture information as a reference for fine-grained restoration of the network, enabling the network to produce clearer reconstructions. Furthermore, we discovered the remarkable capability of the patchwise discriminator to perceive the degradation type of different regions within the acquired remote sensing image. Inspired by this finding, we design a novel degradation representation module (DRM) that can estimate the degradation information from LR images and guide the network to perform adaptive restoration. Comprehensive experimental results demonstrate that our proposed unsupervised blind super-resolution framework achieves state-of-the-art (SOTA) restoration performance. Our code and pretrained models have been uploaded to GitHub (https://github.com/55Dupup/UDASR) for validation.
Keyword ：
Blind super-resolution (BlindSR) Blind super-resolution (BlindSR) Degradation Degradation degradation representation degradation representation Image reconstruction Image reconstruction Image resolution Image resolution Kernel Kernel remote sensing remote sensing Remote sensing Remote sensing Superresolution Superresolution Training Training unsupervised degradation aware unsupervised degradation aware
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| GB/T 7714 | Guo, Wen-Zhong , Weng, Wu-Ding , Chen, Guang-Yong et al. Unsupervised Degradation Aware and Representation for Real-World Remote Sensing Image Super-Resolution [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
| MLA | Guo, Wen-Zhong et al. "Unsupervised Degradation Aware and Representation for Real-World Remote Sensing Image Super-Resolution" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62 (2024) . |
| APA | Guo, Wen-Zhong , Weng, Wu-Ding , Chen, Guang-Yong , Su, Jian-Nan , Gan, Min , Philip Chen, C. L. . Unsupervised Degradation Aware and Representation for Real-World Remote Sensing Image Super-Resolution . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
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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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The ensembles of random projection-based fuzzy-c-means (RP-FCM) can handle high-dimensional data ef-ficiently. However, the performance of these ensemble frameworks is still hindered by some issues, such as misaligned membership matrices, information loss of co-similar matrices, large storage space, unstable ensemble results due to the additional re-clustering, etc. To address these issues, we propose a robust and fuzzy ensemble framework via spectral learning for RP-FCM clustering. After using random projection to generate different dimensional datasets and obtaining the membership matrices via fuzzy-c-means, we first convert these membership matrices into regularized graphs and approximates the affinity matrices of these graphs by spectral matrices. This step not only avoids the alignment problems of membership matrices but also excludes the storage of large-scale graphs. The spectral matrices of the same size are used as the features of membership matrices for the ensemble, avoiding the possible information loss by applying co-similar matrix transformations. More importantly, an optimization model is designed in our framework to learn the fusion of spectral features. In this model, the proportion of each base clustering is adjusted adaptively through a fuzzification exponent, and the effect of outliers is also suppressed by a robust norm. Finally, the Laplacian rank constraint in the model guarantees the ensemble can achieve the exact final partition. An efficient algorithm for this model is derived, and its time complexity and convergence are also analyzed. Competitive experimental results on benchmark data demonstrate the effectiveness of the proposed ensemble framework in comparison to state-of-the-art methods.
Keyword ：
Ensemble Ensemble Fuzzy-c-means Fuzzy-c-means Random projection Random projection Robust Robust Spectral learning Spectral learning
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| GB/T 7714 | Shi, Zhaoyin , Chen, Long , Duan, Junwei et al. Robust and fuzzy ensemble framework via spectral learning for random projection-based fuzzy-c-means clustering [J]. | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2023 , 117 . |
| MLA | Shi, Zhaoyin et al. "Robust and fuzzy ensemble framework via spectral learning for random projection-based fuzzy-c-means clustering" . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 117 (2023) . |
| APA | Shi, Zhaoyin , Chen, Long , Duan, Junwei , Chen, Guangyong , Zhao, Kai . Robust and fuzzy ensemble framework via spectral learning for random projection-based fuzzy-c-means clustering . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2023 , 117 . |
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The radial basis function network-based autoregressive (RBF-AR) model is a powerful statistical model which can be expressed as a linear combination of nonlinear functions and frequently appears in a wide range of application fields. Variable projection algorithm is designed for solving smooth separable optimization problems with least squares form and has been used as an efficient tool for the identification of RBF-AR model. However, in real applications, the observations are usually disturbed by non-Gaussian noise or contain outliers. This often leads to nonlinear regression problems. Since there are both linear and nonlinear parameters in such problems, how to optimize such models is still challenging. In this paper, we design a robust variable projection algorithm for the identification of RBF-AR model. The proposed method takes into account the coupling of the linear and nonlinear parameters of RBF-AR model, which eliminates the linear parameters by solving a linear programming and optimizes the reduced function that only contains nonlinear parameters. Numerical results on RBF-AR model to synthetic data and real-world data confirm the effectiveness of the proposed algorithm.
Keyword ：
nonlinear regression problem nonlinear regression problem RBF-AR model RBF-AR model robust parameter estimation robust parameter estimation variable projection variable projection
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| GB/T 7714 | She, Yuexin , Chen, Guangyong , Gan, Min . A robust variable projection algorithm for RBF-AR model [J]. | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS , 2023 : 22-26 . |
| MLA | She, Yuexin et al. "A robust variable projection algorithm for RBF-AR model" . | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS (2023) : 22-26 . |
| APA | She, Yuexin , Chen, Guangyong , Gan, Min . A robust variable projection algorithm for RBF-AR model . | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS , 2023 , 22-26 . |
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