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Infrared and visible image fusion using Latent Low-Rank Representation

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arXiv 2018年

作者： Li, Hui Wu, Xiao-Jun Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence School of IoT engineering Jiangnan University Wuxi China

Infrared and visible image fusion is an important problem in the field of image fusion which has been applied widely in many fields. To better preserve the useful information from source images, in this paper, we propose a novel image fusion method based on latent low-rank representation(LatLRR) which is simple and effective. Firstly, the source images are decomposed into low-rank parts(global structure) and salient parts(local structure) by LatLRR. Then, the low-rank parts are fused by weighted-average strategy to preserve more contour information. Then, the salient parts are simply fused by sum strategy which is a efficient operation in this fusion framework. Finally, the fused image is obtained by combining the fused low-rank part and the fused salient part. Compared with other fusion methods experimentally, the proposed method has better fusion performance than state-of-the-art fusion methods in both subjective and objective evaluation. The Code of our fusion method is available at https://***/hli1221/imagefusion_Infrared_visible_latlrr. Copyright © 2018, The Authors. All rights reserved.

关键词： Infrared imaging

Feature Space Renormalization for Semi-supervised Learning

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arXiv 2023年

作者： Sun, Jun Mao, Zhongjie Li, Chao Zhou, Chao Wu, Xiao-Jun The School of Artificial Intelligence and Computer Science Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University 1800 Lihu Avenue Jiangsu Wuxi214122 China The Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University 1800 Lihu Avenue Jiangsu Wuxi214122 China

Semi-supervised learning (SSL) has been proven to be a powerful method for leveraging unlabelled data to alleviate models' dependence on large labelled datasets. The common framework among recent approaches is to train the model on a large amount of unlabelled data with consistency regularization to constrain the model predictions to be invariant to input perturbation. However, the existing SSL frameworks still have room for improvement in the consistency regularization method. Instead of regularizing category predictions in the label space as in existing frameworks, this paper proposes a feature space renormalization (FSR) mechanism for SSL. First, we propose a feature space renormalization mechanism to substitute for the commonly used consistency regularization mechanism to learn better discriminative features. To apply this mechanism, we start by building a basic model and an empirical model and then introduce our mechanism to renormalize the feature learning of the basic model with the guidance of the empirical model. Second, we combine the proposed mechanism with pseudo-labelling to obtain a novel effective SSL model named FreMatch. The experimental results show that our method can achieve better performance on a variety of standard SSL benchmark datasets, and the proposed feature space renormalization mechanism can also enhance the performance of other SSL approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

Low-Complexity Channel Estimation Method Based on ISSOR-PCG for Massive MIMO Systems 8th

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Low-Complexity Channel Estimation Method Based on ISSOR-PCG ...

8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019

作者： Zhou, Cheng Li, Zhengquan Wu, Qiong Liu, Yang Li, Baolong Wu, Guilu Zhao, Xiaoqing Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China National Mobile Communication Research Laboratory Southeast University Nanjing210096 China Department of Electronic Engineering Tsinghua University Beijing100084 China The National Key Laboratory of millimeter wave Southeast University Nanjing210096 China

ISBN: (纸本)9789811394089

With the boost of the quantity of antennas at the base station (BS) in massive multiple-input multiple-output (MIMO) systems, the channel capacity and spectral efficiency are also increased. Conventional channel estimation method, such as the classical minimum mean square error (MMSE), which involves the matrix inversion in large size with enormous computational complexity, especially in massive MIMO systems due to large antenna arrays. To degrade the complexity caused by the inversion of the matrix, a low-complexity channel estimation scheme is proposed based on the improved symmetric successive over relaxation preconditioned conjugate gradient (ISSOR-PCG) method to avoid computing the matrix inversion directly. A simple way is also introduced to address the optimal relaxation parameter for the proposed scheme, by utilizing the channel asymptotic orthogonality in massive MIMO systems. Analysis shows that the proposed channel estimator is able to degrade the complexity effectively compared with MMSE channel estimator. Simulation results illustrate that the proposed scheme can obtain near-optimal performance to the classical MMSE estimation method and outperforms other baseline schemes with increased number of iterations. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Mean square error

A Deep Decomposition Network for Image Processing: A Case Study of Image Fusion

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SSRN

SSRN 2022年

作者： Fu, Yu Xu, Tianyang Wu, Xiao-Jun Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China

Image decomposition into constituent components has many applications in the field of image processing. It aims to extract salient features from the source image for subsequent pattern recognition. In this paper, we propose a new image decomposition method based on a convolutional neural network. It is applied to the task of image fusion. In particular, a pair of infrared and visible light images are decomposed into three high-frequency feature images and a low-frequency feature images respectively. The two sets of feature images are fused using a novel fusion strategy to obtain fused feature maps. The feature maps are subsequently reconstructed to obtain the fused image. Compared with the state-of-the-art fusion methods, the proposed approach achieves betterperformance in both subjective and objective evaluation. © 2022, The Authors. All rights reserved.

关键词： Image fusion

LE2Fusion: A novel local edge enhancement module for infrared and visible image fusion

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arXiv 2023年

作者： Xiao, Yongbiao Li, Hui Cheng, Chunyang Song, Xiaoning Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China

Infrared and visible image fusion task aims to generate a fused image which contains salient features and rich texture details from multi-source images. However, under complex illumination conditions, few algorithms pay attention to the edge information of local regions which is crucial for downstream tasks. To this end, we propose a fusion network based on the local edge enhancement, named LE2Fusion. Specifically, a local edge enhancement (LE2) module is proposed to improve the edge information under complex illumination conditions and preserve the essential features of image. For feature extraction, a multi-scale residual attention (MRA) module is applied to extract rich features. Then, with LE2, a set of enhancement weights are generated which are utilized in feature fusion strategy and used to guide the image reconstruction. To better preserve the local detail information and structure information, the pixel intensity loss function based on the local region is also presented. The experiments demonstrate that the proposed method exhibits better fusion performance than the state-of-the-art fusion methods on public datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Image fusion

Mutual Information-Guided GA for Bayesian Network Structure Learning (Extended Abstract)

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Mutual Information-Guided GA for Bayesian Network Structure ...

International Conference on Data engineering

作者： Kefei Yan Wei Fang Hengyang Lu Xin Zhang Jun Sun Xiaojun Wu Department of Computer Science and Technology Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Bayesian network structure learning (BNSL) from data is an NP-hard problem. Genetic algorithms are powerful for solving combinatorial optimization problems, but the lack of effective guidance results in slow convergence and low accuracy regarding BNSL. To address this problem, we propose a mutual information (MI) guided genetic algorithm (MIGA) for BNSL in this paper, which uses MI to effectively search BN structures. In the initialization phase of MIGA, the population is generated by adding additional constraints based on MI to reach a higher score without losing diversity. By employing normalized MI and defining the population support, the potential dominance in the population can be identified and then used to design a novel crossover operator in order to preserve the dominant genes with a higher probability. Moreover, with the guidance of MI for removing loops from the structures, infeasible solutions can be handled in a straightforward and practical way. The proposed MIGA is evaluated on eleven well-known benchmark datasets and compared with four GA-based methods and four other state-of-the-art BNSL algorithms. Experimental results show that MIGA outperforms the compared algorithms in convergence and learning accuracy.

关键词： Accuracy NP-hard problem Sociology Search problems Bayes methods Statistics Optimization

Spatial Residual Layer and Dense Connection Block Enhanced Spatial Temporal Graph Convolutional Network for Skeleton-Based Action recognition

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Spatial Residual Layer and Dense Connection Block Enhanced S...

International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Cong Wu Xiao-Jun Wu Josef Kittler School of IOT Engineering Jiangnan University China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence CVSSP University of Surrey UK

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150246

Recent research has shown that modeling the dynamic joint features of the human body by a graph convolutional network (GCN) is a groundbreaking approach for skeleton-based action recognition, especially for the recognition of the body-motion, human-object and human-human interactions. Nevertheless, how to model and utilize coherent skeleton information comprehensively is still an open problem. In order to capture the rich spatiotemporal information and utilize features more effectively, we introduce a spatial residual layer and a dense connection block enhanced spatial temporal graph convolutional network. More specifically, our work introduces three aspects. Firstly, we extend spatial graph convolution to spatial temporal graph convolution of cross-domain residual to extract more precise and informative spatiotemporal feature, and reduce the training complexity by feature fusion in the, so-called, spatial residual layer. Secondly, instead of simply superimposing multiple similar layers, we use dense connection to take full advantage of the global information. Thirdly, we combine the above mentioned two components to create a spatial temporal graph convolutional network (ST-GCN), referred to as SDGCN. The proposed graph representation has a new structure. We perform extensive experiments on two large datasets: Kinetics and NTU-RGB+D. Our method achieves a great improvement in performance compared to the mainstream methods. We evaluate our method quantitatively and qualitatively, thus proving its effectiveness.

关键词： Convolution Spatiotemporal phenomena Joints Feature extraction Two dimensional displays Three-dimensional displays

CL-Flow:Strengthening the Normalizing Flows by Contrastive Learning for Better Anomaly Detection

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arXiv 2023年

作者： Wang, Shunfeng Li, Yueyang Luo, Haichi Bi, Chenyang Faculty of Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiang-nan University 1800 Lihu Avenue Jiangsu Wuxi214122 China

In the anomaly detection field, the scarcity of anomalous samples has directed the current research emphasis towards unsupervised anomaly detection. While these unsupervised anomaly detection methods offer convenience, they also overlook the crucial prior information embedded within anomalous samples. Moreover, among numerous deep learning methods, supervised methods generally exhibit superior performance compared to unsupervised methods. Considering the reasons mentioned above, we propose a self-supervised anomaly detection approach that combines contrastive learning with 2D-Flow to achieve more precise detection outcomes and expedited inference processes. On one hand, we introduce a novel approach to anomaly synthesis, yielding anomalous samples in accordance with authentic industrial scenarios, alongside their surrogate annotations. On the other hand, having obtained a substantial number of anomalous samples, we enhance the 2D-Flow framework by incorporating contrastive learning, leveraging diverse proxy tasks to fine-tune the network. Our approach enables the network to learn more precise mapping relationships from self-generated labels while retaining the lightweight characteristics of the 2D-Flow. Compared to mainstream unsupervised approaches, our self-supervised method demonstrates superior detection accuracy, fewer additional model parameters, and faster inference speed. Furthermore, the entire training and inference process is end-to-end. Our approach showcases new state-of-the-art results, achieving a performance of 99.6% in image-level AUROC on the MVTecAD dataset and 96.8% in image-level AUROC on the BTAD dataset. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

Label distribution expression recognition algorithm based on asymptotic truth value

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Journal of Measurement Science and Instrumentation 2021年第3期12卷 295-303页

作者： HUANG Hao GE Hongwei School of Artificial Intelligence and Computer Science Jiangnan University Wuxi 214122 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Wuxi 214122 China

Ambiguous expression is a common phenomenon in facial expression recognition(FER).Because of the existence of ambiguous expression,the effect of FER is severely *** reason maybe that the single label of the data cannot effectively describe complex emotional intentions which are vital in *** distribution learning contains more information and is a possible way to solve this *** apply label distribution learning on FER,a label distribution expression recognition algorithm based on asymptotic truth value is *** the premise of not incorporating extraneous quantitative information,the original information of database is fully used to complete the generation and utilization of label ***,in training part,single label learning is used to collect the mean value of the overall distribution of ***,the true value of data label is approached gradually on the granularity of data ***,the whole network model is retrained using the generated label distribution *** results show that this method can improve the accuracy of the network model obviously,and has certain competitiveness compared with the advanced algorithms.

关键词： facial expression recognition(FER) label distributed learning label smoothing ambiguous expression