检索结果-内蒙古大学图书馆

SSRN 2022年

作者： Chen, Kai Yang, Chunfeng Zhou, Zhengyuan Liu, Yao Ji, Tianjiao Sun, Weiya Chen, Yang School of Cyber Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing210096 China The College of Software Engineering Southeast University Nanjing210096 China Laboratory of Image Science and Technology The School of Computer Science and Engineering Southeast University Nanjing210096 China Jiangsu Key Laboratory of Molecular and Functional Imaging Department of Radiology Zhongda Hospital Southeast University Nanjing210009 China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing210096 China NHC Key Laboratory of Medical Virology and Viral Diseases National Institute for Viral Disease Control and Prevention Chinese Center for Disease Control and Prevention Beijing China Beijing Institute of Tracking and Communication Technology Beijing100094 China

Modeling spatio-temporal sequences is an important topic yet challenging for existing neural networks. Most of the current spatio-temporal sequence prediction methods usually capture features separately in temporal and spatial dimensions or employ multiple mutually independent local spatio-temporal graphs to represent a spatio-temporal sequence. The first kind of method mentioned above is difficult to mine the complex spatio-temporal correlations, while the other is limited for the accuracy of long-term predictions. To handle these issues, this paper proposes a Transformer-Improved Graph Convolution Network for spatio-temporal prediction. Specifically, the temporal location encoding method is exploited to derive the spatio-temporal characteristics of the sequence utilizing a spatio-temporal feature fusion network. In addition, a spatio-temporal attention network is developed to enhance the spatio-temporal correlation of the sequence, and the dynamic spatial features of sequence are further extracted through the adaptive graph convolution network. A private dataset and a public dataset are employed to demonstrate the performance of the proposed TIGC-Net. The qualitative and quantitative results show that the proposed TIGC-Net can extract dynamic spatiotemporal properties more effectively, enhance the spatio-temporal correlation of sequences and improve the prediction accuracy compared with four stateof-the-art. © 2022, The Authors. All rights reserved.

关键词： Convolution

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13.2% EQE near-infrared TADF OLED with emission peak at 761 nm

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Chemical Engineering Journal 2023年第Part4期452卷

作者： Tai, Jing-Wen Tang, Yukun Zhang, Kai Yang, Chen-Zong Pan, Ze-Hui Lin, Yu-Ching Shih, Yu-Wei Chen, Chia-Hsun Chiu, Tien-Lung Lee, Jiun-Haw Wang, Chuan-Kui Wu, Chung-Chih Fan, Jian Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices Soochow University Jiangsu Suzhou215123 China Department of Electrical Engineering Graduate Institute of Electronics Engineering and Graduate Institute of Photonics and Optoelectronics National Taiwan University Taipei10617 Taiwan Shandong Province Key Laboratory of Medical Physics and Image Processing Technology School of Physics and Electronics Shandong Normal University Jinan250014 China Department of Chemistry National Taiwan University Taipei10617 Taiwan Department of Electrical Engineering Yuan Ze University Taoyuan32003 Taiwan Graduate Institute of Photonics and Optoelectronics and Department of Electrical Engineering National Taiwan University Taipei10617 Taiwan State Key Laboratory of Structural Chemistry Fujian Institute of Research on Structure of Matter Chinese Academy of Sciences Fujian Fuzhou35002 China

Near infrared (NIR) emitters have attracted much attention due to their advanced application in biomedical sensing/imaging, and telecommunications. However, the development of efficient NIR thermally activated delayed fluorescence (TADF) emitters with the emission over 750 nm is still a challenge. Herein two Y-shaped NIR TADF molecules (Py-TPA and Py-CN-TPA) were introduced. The inclusion of pyridine unit in these two emitters not only extended the conjugated system but also increased the molecular aspect ratios, thereby leading to their high horizontal ratio of emitting dipole orientation (Θ//). In addition, the NIR emitter showed a low Huang-Rhys (HR) factor in thin film relative to that in solution, indicating a low non-radiative transition rate and a high photoluminescent quantum yield (PLQY). Consequently, TADF OLED based on Py-TPA demonstrated a record-high external quantum efficiency (EQE) of 22.2% at 726 nm, 16.3% at 747 nm, and 13.2% at 761 nm. © 2022 Elsevier B.V.

关键词： Aspect ratio

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Dehazed image Quality Assessment by Haze-Line Theory

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Journal of Physics: Conference Series 2017年第1期844卷

作者： Yingchao Song Haibo Luo Rongrong Lu Junkai Ma Shenyang Institute of Automation CAS Shenyang 110016 China University of Chinese Academy of Sciences Beijing 100049 China Key Laboratory of Opto-Electronic Information Processing CAS Shenyang 110016 China The Key Lab of Image Understanding and Computer Vision Liaoning Province Shenyang 110016 China

images captured in bad weather suffer from low contrast and faint color. Recently, plenty of dehazing algorithms have been proposed to enhance visibility and restore color. However, there is a lack of evaluation metrics to assess the performance of these algorithms or rate them. In this paper, an indicator of contrast enhancement is proposed basing on the newly proposed haze-line theory. The theory assumes that colors of a haze-free image are well approximated by a few hundred distinct colors, which form tight clusters in RGB space. The presence of haze makes each color cluster forms a line, which is named haze-line. By using these haze-lines, we assess performance of dehazing algorithms designed to enhance the contrast by measuring the inter-cluster deviations between different colors of dehazed image. Experimental results demonstrated that the proposed Color Contrast (CC) index correlates well with human judgments of image contrast taken in a subjective test on various scene of dehazed images and performs better than state-of-the-art metrics.

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Nearly Unity Quantum Yield Persistent Room-Temperature Phosphorescence from Heavy Atom-Free Rigid Inorganic/Organic Hybrid Frameworks

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Angewandte Chemie 2022年第35期134卷

作者： Xin Zheng Yuanshan Huang Prof. Wei Lv Prof. Jianzhong Fan Prof. Qidan Ling Prof. Zhenghuan Lin Fujian Key Laboratory of Polymer Materials College of Chemistry and Materials Science Fujian Normal University Fuzhou 350007 China Shandong Province Key Laboratory of Medical Physics and Image Processing Technology School of Physics and Electronics Shandong Normal University Jinan 250014 China Fujian Provincial Key Laboratory of Advanced Materials Oriented Chemical Engineering Fuzhou 350007 China

Synergism between covalent and non-covalent bonds is employed to fix an organic phosphor guest in a rigid inorganic framework, simulating the stiffening effect seen in the glassy state and realizing efficient and ultralong room-temperature phosphorescence (RTP). Twelve heavy-atom-free composites have been obtained through introducing arylboric or arylcarboxylic acid derivatives into the inorganic boric acid matrix by solid-phase synthesis. Owing to the stiffening effect of multiple bonds, all the composites show highly efficient and persistent RTP of guest molecules with a quantum yield ranging from 39.8 % to ca. 100 % and a lifetime up to 8.74 s, which results in a 55 s afterglow visible to the naked eye after exposure to a portable UV lamp. Interestingly, it is found that the substitution position and quantity of carboxyl in the guest have a great influence on the phosphorescent properties, and that the heavy-atom effect is invalid in such host–guest hybrid systems. The 100 g grade composite is easily prepared because of the solvent-free, green, and simple synthesis method. These results provide an important way for the development of RTP materials with ultrahigh quantum yield and ultralong lifetime, as well as their practical applications in the fields of anti-counterfeiting and information storage, among others.

关键词： Afterglow Boric Acid Host–Guest Systems Hybrid Materials Room-Temperature Phosphorescence

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SU-F-I-07: CBCT Denoising Based On Adaptive Dictionary Learning Algorithms

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Medical Physics 2016年第6PART7期43卷 3388-3388页

作者： Shaohua Qin Juan Yin Hongsheng Li Jinhu Chen Yong Yin Dengwang Li Shandong Province Key Laboratory of Medical Physics and Image Processing Technology School of Physics and Electronics Shandong Normal University China Medical Engineering Department Shandong Provincial Qianfoshan Hospital China Department of Radiation Oncology Shandong Cancer Hospital and Institute China

Purpose: We proposed a new dictionary learning algorithm (AK-SVD) based on K-SVD. AK-SVD can denoise the CBCT image, and did not need the noise information as prior knowledge. Methods: The AK-SVD had two steps: signal sparse representation, and then dictionary optimization. The CBCT image was sparse, and there were limited big coefficients. The other coefficients were zero or near zero. In the sparse representation step of traditional K-SVD, the noise variance was used as a threshold to select the big representation coefficients. This increased the complexity of the algorithm. The denoising result also was affected by the accuracy of the noise variance estimation, especially in non-Gaussian noise. In AK-SVD we used the average of the existing big coefficients as a threshold. The new found coefficient was compared with the threshold. If it was bigger than this threshold, it will be determined as the big coefficient, and be added to the set of existing big coefficients. The finding process continued. If it was smaller than this threshold, the finding process was *** threshold was not related to the noise variance, and based on this method we improved the traditional K-SVD. Results: In the synthetic experiments about designing dictionary from synthetic signals, the correct rate of dictionary learning by the AK-SVD was similar with the ideal results of the K-SVD where the noise variance was known. However, the AK-SVD algorithm did not need to evaluate the noise variance, so it had lower computational complexity and wider adaptability. In the denoising experiment about the CBCT image corrupted by the non-Gaussian noise, AK-SVD has an advantage in terms of texture. Conclusion: The AK-SVD can work well with the noise variance unknown, and it had lower computational complexity and wider adaptability than K-SVD. This work was jointly supported by National Natural Science Foundation of China (61471226), Natural Science Foundation for Distinguished Young Scholars of Shand

关键词： Cone beam computed tomography Dictionaries Computational complexity Science funding Experiment design Optimization

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Cross-receptive Focused Inference Network for Lightweight image Super-Resolution

arXiv

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arXiv 2022年

作者： Li, Wenjie Li, Juncheng Gao, Guangwei Deng, Weihong Zhou, Jiantao Yang, Jian Qi, Guo-Jun The Intelligent Visual Information Perception Laboratory Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210046 China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China The School of Communication and Information Engineering Shanghai University Shanghai200444 China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing210094 China The Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science Faculty of Science and Technology University of Macau 999078 China The School of Computer Science and Technology Nanjing University of Science and Technology Nanjing210094 China The Research Center for Industries of the Future The School of Engineering Westlake University Hangzhou310024 China OPPO Research SeattleWA98101 United States

Recently, Transformer-based methods have shown impressive performance in single image super-resolution (SISR) tasks due to the ability of global feature extraction. However, the capabilities of Transformers that need to incorporate contextual information to extract features dynamically are neglected. To address this issue, we propose a lightweight Cross-receptive Focused Inference Network (CFIN) that consists of a cascade of CT Blocks mixed with CNN and Transformer. Specifically, in the CT block, we first propose a CNN-based Cross-Scale Information Aggregation Module (CIAM) to enable the model to better focus on potentially helpful information to improve the efficiency of the Transformer phase. Then, we design a novel Cross-receptive Field Guided Transformer (CFGT) to enable the selection of contextual information required for reconstruction by using a modulated convolutional kernel that understands the current semantic information and exploits the information interaction within different self-attention. Extensive experiments have shown that our proposed CFIN can effectively reconstruct images using contextual information, and it can strike a good balance between computational cost and model performance as an efficient model. Source codes will be available at https://***/IVIPLab/CFIN. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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NTIRE 2023 image Shadow Removal Challenge Report

NTIRE 2023 Image Shadow Removal Challenge Report

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Vasluianu, Florin-Alexandru Seizinger, Tim Timofte, Radu Cui, Shuhao Huang, Junshi Tian, Shuman Fan, Mingyuan Zhang, Jiaqi Zhu, Li Wei, Xiaoming Wei, Xiaolin Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Dong, Xiaoyi Zhang, Xi Sheryl Li, Chenghua Leng, Cong Yeo, Woon-Ha Oh, Wang-Taek Lee, Yeo-Reum Ryu, Han-Cheol Luo, Jinting Jiang, Chengzhi Han, Mingyan Wu, Qi Lin, Wenjie Yu, Lei Li, Xinpeng Jiang, Ting Fan, Haoqiang Liu, Shuaicheng Xu, Shuning Song, Binbin Chen, Xiangyu Zhang, Shile Zhou, Jiantao Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Wang, Bo Ren, Jiahuan Luo, Yan Kondo, Yuki Miyata, Riku Yasue, Fuma Naruki, Taito Ukita, Norimichi Chang, Hua-En Yang, Hao-Hsiang Chen, Yi-Chung Chiang, Yuan-Chun Huang, Zhi-Kai Chen, Wei-Ting Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Xianwei, Li Fu, Huiyuan Liu, Chunlin Ma, Huadong Fu, Binglan He, Huiming Wang, Mengjia She, Wenxuan Liu, Yu Nathan, Sabari Kansal, Priya Zhang, Zhongjian Yang, Huabin Wang, Yan Zhang, Yanru Phutke, Shruti S. Kulkarni, Ashutosh Khan, Md Raqib Murala, Subrahmanyam Vipparthi, Santosh Kumar Ye, Heng Liu, Zixi Yang, Xingyi Liu, Songhua Wu, Yinwei Jing, Yongcheng Yu, Qianhao Zheng, Naishan Huang, Jie Long, Yuhang Yao, Mingde Zhao, Feng Zhao, Bowen Ye, Nan Shen, Ning Cao, Yanpeng Xiong, Tong Xia, Weiran Li, Dingwen Xia, Shuchen Computer Vision Lab Ifi Caidas University of Würzburg Germany Computer Vision Lab Eth Zürich Switzerland Meituan Group China Department of Information Technology Uppsala University Sweden Institute of Automation Chinese Academy of Sciences Beijing China Nanjing China Maicro Nanjing China Department of Artificial Intelligence Convergence Sahmyook University Seoul Korea Republic of Megvii Technology China University of Electronic Science and Technology of China China University of Macau China China Toyota Technological Institute Japan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States Beijing University of Post and Teleconmunication Beijing China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education China Couger Inc. Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar Punjab Rupnagar India Research Institute Singapore National University of Singapore Singapore Research Institute Singapore University of Sydney Australia Brain-Inspired Vision Laboratory Information Science and Technology Institution University of Science and Technology of China China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China South China University of Technology China

ISBN: (纸本)9798350302493

This work reviews the results of the NTIRE 2023 Challenge on image Shadow Removal. The described set of solutions were proposed for a novel dataset, which captures a wide range of object-light interactions. It consists of 1200 roughly pixel aligned pairs of real shadow free and shadow affected images, captured in a controlled environment. The data was captured in a white-box setup, using professional equipment for lights and data acquisition sensors. The challenge had a number of 144 participants registered, out of which 19 teams were compared in the final ranking. The proposed solutions extend the work on shadow removal, improving over the performance level describing state-of-the-art methods. © 2023 IEEE.

关键词： Data acquisition

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Violent Interaction Detection in Video Based on Deep Learning

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Journal of Physics: Conference Series 2017年第1期844卷

作者： Peipei Zhou Qinghai Ding Haibo Luo Xinglin Hou Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 University of Chinese Academy of Sciences Beijing 100049 Key Laboratory of Opto-Electronic Information Processing CAS Shenyang 110016 The Key Lab of Image Understanding and Computer Vision Liaoning Province Shenyang 110016 Space star technology co. LTD Beijing 100086

Violent interaction detection is of vital importance in some video surveillance scenarios like railway stations, prisons or psychiatric centres. Existing vision-based methods are mainly based on hand-crafted features such as statistic features between motion regions, leading to a poor adaptability to another dataset. En lightened by the development of convolutional networks on common activity recognition, we construct a FightNet to represent the complicated visual violence interaction. In this paper, a new input modality, image acceleration field is proposed to better extract the motion attributes. Firstly, each video is framed as RGB images. Secondly, optical flow field is computed using the consecutive frames and acceleration field is obtained according to the optical flow field. Thirdly, the FightNet is trained with three kinds of input modalities, i.e., RGB images for spatial networks, optical flow images and acceleration images for temporal networks. By fusing results from different inputs, we conclude whether a video tells a violent event or not. To provide researchers a common ground for comparison, we have collected a violent interaction dataset (VID), containing 2314 videos with 1077 fight ones and 1237 no-fight ones. By comparison with other algorithms, experimental results demonstrate that the proposed model for violent interaction detection shows higher accuracy and better robustness.

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Comprehensive histological imaging of native microbiota in human glioma

Research Square

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Research Square 2021年

作者： Zhao, Jiajia He, Dian Lai, Hei Ming Xu, Yingying Luo, Yunhao Li, Ting Liang, Jianhao Yang, Xiaodu Guo, Linlang Ke, Yiquan Zhou, Hongwei Wu, Wutian Guo, Hongbo Sun, Haitao Microbiome Medicine Center Clinical Biobank Center Department of Laboratory Medicine Zhujiang Hospital Southern Medical University Guangzhou510280 China Neurosurgery Center The National Key Clinical Specialty The Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular Disease Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration The Neurosurgery Institute of Guangdong Province Zhujiang Hospital Southern Medical University Guangzhou510280 China Department of Psychiatry Faculty of Medicine The Chinese University of Hong Kong Shatin Hong Kong School of Biomedical Engineering Guangdong Provincial Key Laboratory of Medical Image Processing Southern Medical University Guangzhou510515 China Department of Pathology Zhujiang Hospital Southern Medical University Guangzhou510280 China Jinan University Guangzhou510632 China Re-Stem Biotechnology Co. Ltd Suzhou215300 China Key Laboratory of Mental Health of the Ministry of Education Guangdong–Hong Kong–Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence Southern Medical University Guangzhou510515 China

Mounting evidence suggests that distinct microbial communities reside in tumors and play important roles in tumor physiology. Recently, Nejman et al. profiled the composition and localization of intratumoral bacteria using 16S DNA sequencing and histological visualization methods across seven tumor types, including human glioblastoma. However, considering potential contamination in their sample origins and processing, the results based on traditional histological methods need to be validated. Here, we propose a three-dimensional (3D) intratumoral microbiota visualization and quantification protocol to observe microbiota in intact tumor tissues on the premise of avoiding possible contamination in the surface of tissues, based on tissue clearing, immunofluorescent labeling, microscopy imaging, and image processing. For the first time, we have achieved 3D quantitative imaging of bacterial LPS fluorescent signals deep in gliomas in a contamination-free manner, which was founded mostly localized near nuclear membranes or in the intercellular space. Through an automated statistical algorithm, reliable signals can be distinguished for further analysis of their sizes, distribution, and fluorescence intensities. Combining two-dimensional images from multiple thin-section histological methods, including immunochemistry and fluorescence in situ hybridization, we provide a comprehensive histological investigation of the morphology and distribution of these signals on human glioma samples. We expect that this multi-evidence chain will provide supporting proof for the presence of intratumoral bacteria in human glioma and that the integrated pipeline can be applied to investigate the native bacteria within diverse tumors and contribute to the interpretation of their direct roles in the tumor microenvironment. © 2021, CC BY.

关键词： Tumors

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High Dynamic Range Imaging Using Multiple Exposures

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Journal of Physics: Conference Series 2017年第1期844卷

作者： Xinglin Hou Haibo Luo Peipei Zhou Wei Zhou Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 University of Chinese Academy of Sciences Beijing 100049 Key Laboratory of Opto-Electronic Information Processing CAS Shenyang 110016 The Key Lab of Image Understanding and Computer Vision Liaoning Province Shenyang 110016 AVIC Jiangxi HONGDU Aviation Industry Group LTD Nanchang China

It is challenging to capture a high-dynamic range (HDR) scene using a low-dynamic range (LDR) camera. This paper presents an approach for improving the dynamic range of cameras by using multiple exposure images of same scene taken under different exposure times. First, the camera response function (CRF) is recovered by solving a high-order polynomial in which only the ratios of the exposures are used. Then, the HDR radiance image is reconstructed by weighted summation of the each radiance maps. After that, a novel local tone mapping (TM) operator is proposed for the display of the HDR radiance image. By solving the high-order polynomial, the CRF can be recovered quickly and easily. Taken the local image feature and characteristic of histogram statics into consideration, the proposed TM operator could preserve the local details efficiently. Experimental result demonstrates the effectiveness of our method. By comparison, the method outperforms other methods in terms of imaging quality.

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