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arXiv 2020年

作者： Seungjun, Nah Sanghyun, Son Radu, Timofte Kyoung Mu, Lee Tseng, Yu Xu, Yu-Syuan Chiang, Cheng-Ming Tsai, Yi-Min Brehm, Stephan Scherer, Sebastian Xu, Dejia Chu, Yihao Sun, Qingyan Jiang, Jiaqin Duan, Lunhao Yao, Jian Purohit, Kuldeep Suin, Maitreya Rajagopalan, A.N. Ito, Yuichi Hrishikesh, P.S. Puthussery, Densen Akhil, K.A. Jiji, C.V. Kim, Guisik Deepa, P.L. Xiong, Zhiwei Huang, Jie Liu, Dong Kim, Sangmin Nam, Hyungjoon Kim, Jisu Jeong, Jechang Huang, Shihua Fan, Yuchen Yu, Jiahui Yu, Haichao Huang, Thomas S. Zhou, Ya Li, Xin Liu, Sen Chen, Zhibo Dutta, Saikat Das, Sourya Dipta Garg, Shivam Sprague, Daniel Patel, Bhrij Huck, Thomas Department of ECE ASRI SNU Korea Republic of Computer Vision Lab ETH Zurich Switzerland MediaTek Inc University of Augsburg Chair for Multimedia Computing and Computer Vision Lab Germany Peking University China Beijing University of Posts and Telecommunications China Beijing Jiaotong University China Wuhan University China Indian Institute of Technology Madras India Vermilion College of Engineering Trivandrum India CVML Chung-Ang University Korea Republic of APJ Abdul Kalam Technological University India University of Science and Technology of China China Image Communication Signal Processing Laboratory Hanyang University Korea Republic of Southern University of Science and Technology China University of Illinois at Urbana-Champaign United States CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China China IIT Madra Jadavpur University India University of Texas Austin United States Duke University Computer Science Department United States

Motion blur is one of the most common degradation artifacts in dynamic scene photography. This paper reviews the NTIRE 2020 Challenge on image and Video Deblurring. In this challenge, we present the evaluation results from 3 competition tracks as well as the proposed solutions. Track 1 aims to develop single-image deblurring methods focusing on restoration quality. On Track 2, the image deblurring methods are executed on a mobile platform to find the balance of the running speed and the restoration accuracy. Track 3 targets developing video deblurring methods that exploit the temporal relation between input frames. In each competition, there were 163, 135, and 102 registered participants and in the final testing phase, 9, 4, and 7 teams competed. The winning methods demonstrate the state-of-the-art performance on image and video deblurring tasks. Copyright © 2020, The Authors. All rights reserved.

关键词： image enhancement

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Automated Selection of Evolutionary Multi-objective Optimization Algorithms

Automated Selection of Evolutionary Multi-objective Optimiza...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Ye Tian Shichen Peng Tobias Rodemann Xingyi Zhang Yaochu Jin Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Honda Research Institute Europe Offenbach/Main Germany Department of Computer Science University of Surrey Guildford Surrey United Kingdom

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

In the last two decades, many evolutionary algorithms have shown promising performance in solving a variety of multi-objective optimization problems (MOPs). Since there does not exist an evolutionary algorithm having the best performance on all the MOPs, it is unreasonable to use a single evolutionary algorithm to tackle all the MOPs. Since many real-world MOPs are computationally expensive, selecting the best evolutionary algorithm from multiple candidates via empirical comparisons is also impractical. To address the above issues, this paper proposes an automated algorithm selection method for choosing the most suitable evolutionary algorithm for a given MOP. The proposed method establishes a predictor based on the performance of a set of candidate evolutionary algorithms on multiple benchmark MOPs, where the inputs of the predictor are the explicit and implicit features of an MOP, and the output is the index of the evolutionary algorithm having the best performance on the MOP. Experimental results indicate that the evolutionary algorithm suggested by the proposed method is highly competitive among all the candidate evolutionary algorithms, demonstrating the practical value of the proposed method for engineers to select an evolutionary algorithm for their applications.

关键词： Evolutionary computation Benchmark testing Feature extraction Optimization Prediction algorithms Convergence Linear programming

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MOF-based high-entropy-alloy evaporator featuring enhanced interband transitions for efficient solar steam and green electricity generation

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Chemical Engineering Journal 2025年 517卷

作者： Chenyang Dai Jia-Han Zhang Cheng-Yu He Rongrong Dai Likun Pan Yusuke Yamauchi Jing Zhang Zhengtong Li Xingtao Xu Marine Science and Technology College Zhejiang Ocean University Zhoushan 316022 China Electronic-Photonic Smart Sensing Device R&D Team Inner Mongolia Key Laboratory of Intelligent Communication and Sensing and Signal Processing School of Electronic Information Engineering Inner Mongolia University Hohhot 010021 China Research Center of Resource Chemistry and Energy Materials State Key Laboratory of Solid Lubrication Lanzhou Institute of Chemical Physics Chinese Academy of Sciences Lanzhou China Shanghai Key Laboratory of Magnetic Resonance School of Physics and Electronic Science East China Normal University Shanghai 200026 China Department of Materials Process Engineering Graduate School of Engineering Nagoya University Nagoya 464-8603 Japan Australian Institute for Bioengineering and Nanotechnology (AIBN) The University of Queensland Brisbane QLD 4072 Australia Department of Chemical and Biomolecular Engineering Yonsei University 50 Yonsei-ro Seodaemun-gu Seoul 03722 South Korea Ningbo Key Laboratory of Green Petrochemical Carbon Emission Reduction Technology and Equipment Zhejiang Institute of Tianjin University Ningbo Zhejiang 315201 China State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering Yangtze Institute for Conservation and Development Hohai University Nanjing 210098 China

Photothermal materials exhibiting broadband optical absorption and high energy efficiency are highly sought after in the field of solar-driven evaporation. Herein, we present a novel photothermal material derived from polymetallic metal–organic frameworks (MOFs) through a d - d interband transition ( d - d IBT) engineering strategy. This approach enables comprehensive occupation of electronic states within ±4 eV relative to the Fermi level through 3d transition metal coordination, achieving an exceptional average solar absorption efficiency of over 97 % across the full solar spectrum (250–2500 nm). This breakthrough effectively overcomes the inherent wide bandgap limitation of MOFs that traditionally restricts their full-spectrum solar utilization. Leveraging its remarkable broadband sunlight harvesting capacity and ultrafast localized heating performance, this evaporator engineered with high-entropy alloy nanoparticles, achieves an evaporation efficiency surpassing 95% under one sun irradiation while maintaining a high evaporation rate of 2.80 kg m −2 h −1 . Moreover, this evaporator demonstrates exceptional desalination efficiency even with high-salinity seawater (20 wt%), while the waste heat generated during operation can be strategically repurposed to produce green electricity, enhancing its sustainability profile. Thus, the FeCoNiMnCuAlZn high-entropy alloy nanoparticle-based photothermal evaporator offers innovative solutions for addressing the scarcity of clean water resources and the need for green energy.

关键词： Green electricity generation High-entropy alloys Interband transition Metal–organic frameworks Solar steam generator

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ThreeF-Net: Fine-grained feature fusion network for breast ultrasound image segmentation

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Computers in Biology and Medicine 2025年 194卷

作者： Xuesheng Bian Jia Liu Sen Xu Weiquan Liu Leyi Mei Chaoshen Xiao Fan Yang School Of Information Engineering Yancheng Institute Of Technology Hope Avenue Middle Road Yancheng 224051 Jiangsu China College of Computer Engineering Jimei University Yinjiang Road No. 183 Xiamen 361005 Fujian China National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility) Key Laboratory of Genetic Evolution & Animal Models and National Resource Center for Nonhuman Primates Kunming Institute of Zoology Chinese Academy of Sciences East Jiaochang Road No. 32 Kunming 650221 Yunnan China National Key Laboratory of Radar Signal Processing Xidian university No. 2 South Taibai Road Xian 710071 Shaanxi China School of Resources and Environmental Engineering Wuhan University of Science and Technology 947 Heping Avenue Qingshan District Xian 430081 Wuhan China

Convolutional Neural Networks (CNNs) have achieved remarkable success in breast ultrasound image segmentation, but they still face several challenges when dealing with breast lesions. Due to the limitations of CNNs in modeling long-range dependencies, they often perform poorly in handling issues such as similar intensity distributions, irregular lesion shapes, and blurry boundaries, leading to low segmentation accuracy. To address these issues, we propose the ThreeF-Net, a fine-grained feature fusion network. This network combines the advantages of CNNs and Transformers, aiming to simultaneously capture local features and model long-range dependencies, thereby improving the accuracy and stability of segmentation tasks. Specifically, we designed a Transformer-assisted Dual Encoder Architecture (TDE), which integrates convolutional modules and self-attention modules to achieve collaborative learning of local and global features. Additionally, we designed a Global Group Feature Extraction (GGFE) module, which effectively fuses the features learned by CNNs and Transformers, enhancing feature representation ability. To further improve model performance, we also introduced a Dynamic Fine-grained Convolution (DFC) module, which significantly improves lesion boundary segmentation accuracy by dynamically adjusting convolution kernels and capturing multi-scale features. Comparative experiments with state-of-the-art segmentation methods on three public breast ultrasound datasets demonstrate that ThreeF-Net outperforms existing methods across multiple key evaluation metrics.

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Fusarium Wilt Inspection for Phalaenopsis Using Uniform Interval Hyperspectral Band Selection Techniques

Fusarium Wilt Inspection for Phalaenopsis Using Uniform Inte...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Bo-Han Chen Yen-Chieh Ouyang Mang Ou-Yang Horng-Yuh Guo Tsang-Sen Liu Hsian-Min Chen Chao-Cheng Wu Chia-Hsien Wen Chgein-I Chang Min-Shao Shih National Chung Hsing University Taichung Taiwan National Chiao-Tung University Hsinchu City Taiwan Taiwan Agriculture Research Institute Nan-Tou Taiwan Center for Quantitative Imaging in Medicine Taichung Veterans General Hospital Taichung Taiwan National Taipei University of Technology Taipei Taiwan Providence University Taichung Taiwan Remote Sensing Signal and Image Processing Laboratory University of Maryland Baltimore County Baltimore MD USA

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

In this paper, we propose a method to inspect the quality of Phalaenopsis by using hyperspectral imaging techniques. Phalaenopsis is easy to get infected with Fusarium wilt. We use the k-means clustering method to find out that the reflection spectrum of Phalaenopsis stem changes. The methods of the Spectral Angle Mapper (SAM) and Constrained Energy Minimization (CEM) are then used to find the area of the infected area. The Harsanyi, Farrand and Chang (HFC) methods and virtual dimensions (VD) are used to estimate the amount of spectrum required for band selection (BS). Band priority (BP) is used to calculate the priority of each band, and band de-correlation (BD) will remove band data with high correlation with each other. Then use the support vector machine (SVM) to detect Phalaenopsis wilt. The detection accuracy of VNIR and SWIR is 0.81 and 0.86, respectively, with band selection.

关键词： Hyperspectral imaging Support vector machines Hybrid fiber coaxial cables Feature extraction Diseases Correlation signal to noise ratio

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Monaural speech enhancement using deep multi-branch residual network with 1-D causal dilated convolutions

arXiv

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arXiv 2019年

作者： Zhang, Qiquan Nicolson, Aaron Wang, Mingjiang Paliwal, Kuldip K. Wang, Chenxu School of Electronic & Information Engineering Harbin Institute of Technology Shenzhen518055 China Signal Processing Laboratory Griffith University BrisbaneQLD4111 School of Information Science & Engineering Harbin Institute of Technology Weihai264209 China

Deep learning has achieved substantial improvement on single-channel speech enhancement tasks. However, the performance of multi-layer perceptions (MLPs)-based methods is limited by its ability to capture the long-term effective history information. Long short-term memory (LSTM) model is limited by its high computational complexity and the exploding/vanish gradients problem. In this paper, we propose a novel convolution neural network (CNN) architecture, called multi-branch residual network (MB-ResNet), for monaural speech enhancement. The MB-ResNet exploits split-transform-aggregate design, which is expected to obtain strong representational power at a low computational complexity. The combination of causal dilated one dimensional CNN and residual connection significantly expands receptive fields, which allows for capturing extremely long-term temporal contextual information. Our extensive experimental investigation suggest that the MB-ResNets outperform the residual long short-term memory networks (ResLSTMs) and the CNN networks that employ residual or dense aggregations in terms of speech intelligibility and quality, while providing superior parameter efficiency. Furthermore, our experimental results demonstrate that our proposed MB-ResNet model is able to outperform multiple state-of-the-art deep learning-based speech enhancement methods in terms of five widely used objective metrics. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

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Understanding metric-related pitfalls in image analysis validation

arXiv

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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： Computer vision

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A protocol for structured illumination microscopy with minimal reconstruction artifacts

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生物物理学报 2019年第2期5卷 80-90页

作者： Junchao Fan Xiaoshuai Huang Liuju Li Shan Tan Liangyi Chen Key Laboratory of Image Processing and Intelligent Control of Ministry of Education of China School of Automation Huazhong University of Science and Technology Wuhan 430074 China State Key Laboratory of Membrane Biology Beijing Key Laboratory of Cardiometabolic Molecular MedicineInstitute of Molecular Medicine Peking University Beijing 100084 China

The imaging rate of structured illumination microscopy (SIM) reached 188 Hz *** the exposure time decreases,the camera detects fewer virtual photons,while the noise level remains the *** a result,the signal-to-noise ratio (SNR) decreases ***,the SNR decreases further because of photobleaching and *** decreased quality of SIM raw data may lead to surprising artifacts with various causes,which may confuse a new user of SIM *** summarize three significant possible sources of severe artifacts in reconstructed super-resolution (SR) *** motion of a biological sample or an uneven illumination pattern is the most difficult to be *** estimated parameter could also be incorrect,leading to artifact of regular ***,reconstruction with the Wiener method generates stochastic artifacts due to the amplification of noise during the deconvolution *** deal with these problems,we have established a protocol to reconstruct ultrafast SIM raw data obtained in low SNR ***,we checked the quality of the raw data with the imageJ plugin SIMcheck before ***,a modified parameter estimation method was used to improve the precision of the ***,an iterative algorithm was used for SIM reconstruction under low signal-to-noise ratio *** procedure effectively suppressed the artifacts in the super-resolution images reconstructed from raw data of low signal-to-noise ratio.

关键词： Ultrafast SIM Low signal-to-noise ratio Reconstruction

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Input impedance of a microstrip antenna with a chiral substrate based on left-handed spirals

Input impedance of a microstrip antenna with a chiral substr...

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IET International Radar Conference 2018, IRC 2018

作者： Abramov, Vladimir Y. Klyuev, Dmitriy S. Neshcheret, Anatoly M. Osipov, Oleg V. Potapov, Alexander A. Volga State University of Telecommunications and Informatics Samara Russia V.A. Kotel'nikov Institute of Radio Engineering and Electronics Russian Academy of Sciences Moscow Russia JNU-IREE Joint Laboratory of Fractal Method and Signal Processing Department of Electronic Engineering College of Information Science and Technology JiNan University Guangzhou China

This study proposes a mathematical model and a technique for the electrodynamic analysis of a microstrip antenna with a chiral substrate based on the left-handed spirals. It describes the design of such an antenna and presents its impedance characteristics for various parameters. It has been shown that the use of a chiral substrate improves the mass-dimensional characteristics of the microstrip antennas. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Microstrip antennas

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Realization of remote sensing image segmentation based on K-means clustering

Realization of remote sensing image segmentation based on K-...

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第二届材料科学应用与能源材料国际研讨会

作者： Yanhong Wang Denghui Li Yuying Wang Guangxi Key Lab of Wireless Wideband Communication & Signal Processing Institute Of Information Technology of Guilin University of Electronic Technology Key Laboratory of Cognitive Radio and Information Processing Ministry of Education(Guilin University of Electronic Technology) GUET School of Computer Science and Information Security of GUET

Segmentation of remote sensing image is the key technology of positioning ***,we transform the remote sensing image from RGB pace to Lab ***,three centres are iterated by using K-means ***,in order to eliminate the influence,the closed operation of mathematical morphology is used to correct the segmented *** results show that it can segment the road from the remote sensing image in lab mode,by using K-means clustering ***,Lab mode is more suitable for k-mean than other modes.

关键词： Realization of remote sensing image segmentation based on K-means clustering image

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