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Regularization with multilevel non-stationary tight framelets for image restoration

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arXiv 2021年

作者： Li, Yan-Ran Chan, Raymond H.F. Shen, Lixin Zhuang, Xiaosheng College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China Department of Mathematics City University of Hong Kong Tat Chee Avenue Kowloon Tong Hong Kong Department of Mathematics Syracuse University SyracuseNY13244 United States

Variational regularization models are one of the popular and efficient approaches for image restoration. The regularization functional in the model carries prior knowledge about the image to be restored. The prior knowledge, in particular for natural images, are the first-order (i.e. variance in luminance) and second-order (i.e. contrast and texture) information. In this paper, we propose a model for image restoration, using a multilevel non-stationary tight framelet system that can capture the image's first-order and second-order information. We develop an algorithm to solve the proposed model and the numerical experiments show that the model is effective and efficient as compared to other higher-order models. Copyright © 2021, The Authors. All rights reserved.

关键词： Image reconstruction

GRATIS: Deep Learning Graph Representation with Task-specific Topology and Multi-dimensional Edge Features

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arXiv 2022年

作者： Song, Siyang Song, Yuxin Luo, Cheng Song, Zhiyuan Kuzucu, Selim Jia, Xi Guo, Zhijiang Xie, Weicheng Shen, Linlin Gunes, Hatice The Department of Computer Science and Technology University of Cambridge CambridgeCB3 0FT United Kingdom Baidu Inc Beijing100193 China Computer Vision Institute School of Computer Science & Software Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China Department of Computer Engineering Middle East Technical University Ankara Turkey School of Computer Science University of Birmingham Birmingham United Kingdom

Graph is powerful for representing various types of real-world data. The topology (edges’ presence) and edges’ features of a graph decides the message passing mechanism among vertices within the graph. While most existing approaches only manually define a single-value edge to describe the connectivity or strength of association between a pair of vertices, task-specific and crucial relationship cues may be disregarded by such manually defined topology and single-value edge features. In this paper, we propose the first general graph representation learning framework (called GRATIS) which can generate a strong graph representation with a task-specific topology and task-specific multi-dimensional edge features from any arbitrary input (i.e., graph or non-graph data). To learn each edge’s presence and multi-dimensional feature, our framework takes both of the corresponding vertices pair and their global contextual information into consideration, enabling the generated graph representation to have a globally optimal message passing mechanism for different down-stream tasks. The principled investigation results achieved for various graph analysis tasks (e.g., graph classification, vertex classification, and link prediction) on 11 graph and non-graph datasets show that our GRATIS can not only largely enhance pre-defined graphs but also learns a strong graph representation for non-graph data, with clear performance improvements on all tasks. In particular, the learned topology and multi-dimensional edge features provide complementary task-related cues for graph analysis tasks. Our framework is effective, robust and flexible, and is a plug-and-play module that can be combined with different backbones and Graph Neural Networks (GNNs) to generate a task-specific graph representation from various graph and non-graph data. Our code is made publicly available at https://***/SSYSteve/Learning-Graph-Representation-with-Task-specific-Topology-and-Multi-dimensional-Edge-Feature

关键词： Graph neural networks

Hybrid Video and Image Hashing for Robust Face Retrieval

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Hybrid Video and Image Hashing for Robust Face Retrieval

International Conference on Automatic Face and Gesture recognition

作者： Ruikui Wang Shishi Qiao Ruiping Wang Shiguang Shan Xilin Chen Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728130798

ISBN: (纸本)9781728130804

Video face retrieval (VFR) is an appealing and practical computer vision task, which aims to search particular character from masses of videos like in TV-Series. The challenges of this task mainly lie in two aspects, i.e. faces in such videos contain complex appearance variations with uncontrollable shooting environment and searching from big data usually requires high efficiency in both space and time. To fulfill this task, current works typically proceed in a learning to hash manner by fusing single-frame features within a video to obtain the video representation and further embedding it into Hamming space to yield video binary codes. The feature fusion stage has inevitably discarded too much frame information and leads to less discriminative video codes. In this paper, we propose Hybrid Video and Image Hashing (HVIH) to learn more effective binary codes for face videos. Specifically, we fully exploit the dense frame features rather than simply discarding them after the video level fusion and jointly optimize binary codes for the video and its composed frames in adapted supervised manners. To achieve more robust video representation, we introduce a module of video center alignment to ensure the binary codes location of the video and its frames to be as compact and consistent as possible in the Hamming space, which naturally facilitates both tasks of video-to-video retrieval and image-to-video retrieval. Extensive experiments on two challenging video face databases demonstrate the superiority of our approach over the state-of-the-art.

关键词： Binary codes Face recognition Faces Task analysis Feature extraction Databases Robustness

Sensorless Speed Control of PMSMs based on an Improved Particle Filter

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Sensorless Speed Control of PMSMs based on an Improved Parti...

2019 IEEE International Conference on Electrical, Control and Instrumentation Engineering, ICECIE 2019

作者： Ren, Hai-Peng Nan, Zi-Yuan Li, Jie Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'An University of Technology Xi'an China Xi'An University of Technology Department of Electrical Engineering Xi'an China

ISBN: (纸本)9781728139395

Position sensors in permanent magnet synchronous motor (PMSM) are restrained in some applications because of the space restriction and the reliability of the system. A marginalized particle filter (MPF) can used to staminate the speed of the motor by combining a Kalman filter (KF) with a particle filter (PF). In the MPF algorithm, the rotor position of the permanent magnet synchronous motor (PMSM) is represented by a set of particles, and the rotor speed associated with each particle is estimated by using the KF. The PF here is used to handle the non-Gaussianity and nonlinearity of the system. In this paper, the uniform distribution of particles is proposed to replace the traditional Gaussian distribution of particles in the PF to get better performance at the low-speed range. The motor drive system prototype is built using a TMS320F28335 digital signal processor as a controller core. The proposed improved PF is used in the sensorless speed vector control PMSM system. The experimental results show that the proposed PF with a uniform distribution of initial particles enables more accurate speed estimation in the low-speed range compared to the conventional Gaussian distribution, while increasing number of particles also helps to improve the accuracy. © 2019 IEEE.

关键词： Kalman filters

Neuron segmentation using 3D wavelet integrated encoder-decoder network

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arXiv 2021年

作者： Li, Qiufu Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shen zhen University Shenzhen518060 China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China Marshall Laboratory of Biomedical Engineering Shenzhen University Shenzhen518060 China

Motivation: 3D neuron segmentation is a key step for the neuron digital reconstruction, which is essential for exploring brain circuits and understanding brain functions. However, the fine line-shaped nerve fibers of neuron could spread in a large region, which brings great computational cost to the neuron segmentation. Meanwhile, the strong noises and disconnected nerve fibers bring great challenges to the task. Results: In this paper, we propose a 3D wavelet and deep learning based 3D neuron segmentation method. The neuronal image is first partitioned into neuronal cubes to simplify the segmentation task. Then, we design 3D WaveUNet, the first 3D wavelet integrated encoder-decoder network, to segment the nerve fibers in the cubes;the wavelets could assist the deep networks in suppressing data noises and connecting the broken fibers. We also produce a Neuronal Cube Dataset (NeuCuDa) using the biggest available annotated neuronal image dataset, BigNeuron, to train 3D WaveUNet. Finally, the nerve fibers segmented in cubes are assembled to generate the complete neuron, which is digitally reconstructed using an available automatic tracing algorithm. The experimental results show that our neuron segmentation method could completely extract the target neuron in noisy neuronal images. The integrated 3D wavelets can efficiently improve the performance of 3D neuron segmentation and reconstruction. © 2021, CC BY.

关键词： Neurons

A Novel Group-based Swarm Optimizer for Large-Scale Optimization

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A Novel Group-based Swarm Optimizer for Large-Scale Optimiza...

International Workshop on Advanced Computational Intelligence (IWACI)

作者： Shanwen Guan Rushi Lan Yijie Zhu Ruomei Wang Guangxi Key Laboratory of Image and Graphic intelligent processing Guilin University of Electronic Technology Guilin China School of Information and Communication Guilin University of Electronic Technology Guilin China

ISBN: (数字)9781728142487

ISBN: (纸本)9781728142494

In this paper, a novel group-based swarm optimizer (GSO) is proposed for large scale optimization. Inspired from the group learning behavior in human society, the hierarchical learning are involved in GSO. In the mass learning stage, GSO randomly picks three particles to form a study group and then adopts the competitive mechanism to update the members of studing group. In the hierarchical learning phase, elite particles, the better fitness value, are directly retained for the next iteration. Worst particles learn two dominant particles to further search for more promising areas in the team at the same time. Another particles utilize differential evolution to explore search space. Then, theoretically and empirically analysis of GSO are presented and the exploration abilities of proposed method are performed compared the most popular particle swarm optimizer. Further, a set of experiments on two widely used large-scale benchmark sets demonstrate that GSO achieves better performance on large scale problems than several state-of-the-art algorithms.

关键词： Optimization Convergence Graphics Space exploration Sociology Statistics Next generation networking

Technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

Clustering-Based Representation Learning through Output Translation and Its Application to Remote-Sensing Images

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arXiv 2021年

作者： Li, Qinglin Li, Bin Garibaldi, Jonathan M. Qiu, Guoping College of Electronic and Information Engineering Shenzhen University Shenzhen518052 China Guangdong Key Lab for Intelligent Information Processing Shenzhen University Shenzhen518052 China School of Computer Science The University of Nottingham NottinghamNG8 1BB United Kingdom Shenzhen Institute of AI and Robotics for Society Shenzhen518172 China Pengcheng Laboratory Shenzhen518055 China

In supervised deep learning, learning good representations for remote-sensing images (RSI) relies on manual annotations. However, in the area of remote sensing, it is hard to obtain huge amounts of labeled data. Recently, self-supervised learning shows its outstanding capability to learn representations of images, especially the methods of instance discrimination. Comparing methods of instance discrimination, clustering-based methods not only view the transformations of the same image as "positive" samples but also similar images. In this paper, we propose a new clustering-based method for representation learning. We first introduce a quantity to measure representations' discriminativeness and from which we show that even distribution requires the most discriminative representations. This provides a theoretical insight into why evenly distributing the images works well. We notice that only the even distributions that preserve representations' neighborhood relations are desirable. Therefore, we develop an algorithm that translates the outputs of a neural network to achieve the goal of evenly distributing the samples while preserving outputs' neighborhood relations. Extensive experiments have demonstrated that our method can learn representations that are as good as or better than the state of the art approaches, and that our method performs computationally efficiently and robustly on various RSI datasets. The paper has been published on Remote Sensing https://***/10.3390/rs14143361. Copyright © 2021, The Authors. All rights reserved.

关键词： Remote sensing

No-Reference Image Quality Assessment Based on Multi-scale Convolutional Neural Networks

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No-Reference Image Quality Assessment Based on Multi-scale C...

Computing Conference, 2019

作者： Chen, Peikun Niu, Yuzhen Huang, Dong College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China

ISBN: (纸本)9783030228675

Big data brings more challenges to image quality assessment (IQA). First of all, reference images are not always available, so full-reference IQA (FR-IQA) and reduced-reference IQA (RR-IQA) can be problematic for big data. Because of no requirement for any pristine knowledge, no-reference IQA (NR-IQA) has recently received a great deal of attention. In recent years, deep convolution neural network (CNN) has achieved many successes in visual recognition tasks. Researches in literature have shown that NR-IQA metrics can be learned from features extracted using deep CNN. But existing CNN-based IQA models focus on local spatial information but ignore global spatial structure. In this paper, we propose a NR-IQA model learns from multi-scale CNN (MSCNN). Multi-scale mechanism has boosted the performances of many singe-scale methods in a variety of applications. MSCNN is a multi-scale CNN model which uses fully connected layers on top of multiple single-scale CNN models which responsible for feature extraction at different scales. In this way, the features extracted at multiple scales work together to assess image quality. Extensive experiments are conducted on the LIVE database and the experimental results validate the effectiveness of our MSCNN index compared to typical FR-IQA and state-of-the-art NR-IQA metrics. Furthermore, the results of cross database experiments show that MSCNN has good generalization ability. © 2019, Springer Nature Switzerland AG.

关键词： Big data