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An Image Denoising Method Based on Deep Residual GAN

Journal of Physics: Conference Series 2020年第3期1550卷

作者： Ziyuan Wang Lidan Wang Shukai Duan Yunfei Li Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing 400715 China Schoolof Electronic and information engineering Southwest University Chongqing400715 China College of Artificiall Intelligence Southwest University Chongqing 400715 China Braininspired Computing & Intelligent Control of Chongqing Key Lab Chongqing 400715 China National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715 China Chongqing Brain Science Collaborative Innovation Center Chongqing 400715 China

As people come into contact with image data more often, high quality and clear images attract more attention. Many methods have been proposed to deal with image noise problem including deep learning (DL). However most of them is lack of capability when customers want more perceptual details of the image without information loss. In this paper, a deep residual network based on generative adversarial (GAN) network was proposed to complete the image denoising mission. Firstly, a generative-adversarial network structure based on residual blocks was designed. Secondly, a refined loss function was given to train the GAN network. The well designed loss function can help the generated image to be very close to the clear counterpart (ground truth) while enhancing more details in colours and brightness. Finally, extensive experiments show that our network is not only convincing for images denoising, but also effective for other image process tasks, such as image defogging, medical CT denoising etc., presenting impressive and competitive effects.

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Fast simulations of electromagnetic scattering from 1-D rough surface through adaptively modified characteristic basis function method

Fast simulations of electromagnetic scattering from 1-D roug...

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International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications (MAPE)

作者： Jing Jing Wang An Qi Wang Zhi Xiang Huang Key Laboratory of intelligent Computing & Signal Processing Anhui University Hefei China

In this paper, the Adaptively Modified Characteristic Basis Function Method (AMCBFM) is proposed to fast simulate the electromagnetic scattering from one-dimensional rough surface. Firstly, the Primary Characteristic Basis Functions (PCBFs) arising from the self-interaction within the self-block are generated, and the coefficients of the basis function and the first current of each block are obtained. Secondly, the Secondary Characteristic Basis Functions (SCBFs) which account for the mutual coupling effects from the other distinct domains are obtained by using the inter-impedances. The higher SCBFs can be also derived through the same way. Finally, a new precision method is applied to control the current error, which is used to determine the order of the higher order SCBFs. By Comparing numerical simulations through the AMCBFM and the traditional Characteristic Basis Function Method (CBFM), the AMCBFM can guarantee the accuracy and void higher iteration, and the convergence performance is better than the CBFM in the lower order SCBFs. Moreover, by comparing numerical results of the AMCBFM and the method of moments (MoM), the AMCBFM can effectively reduce the size of the impedance matrix, and significantly reduce computing time.

关键词： Electromagnetic scattering Rough surfaces Surface roughness Method of moments Adaptation models Nickel

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Design of Two-turns Loop Antenna with Magnetic Core for Efficient Wireless Powering of Medical Telemetry

Design of Two-turns Loop Antenna with Magnetic Core for Effi...

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International Applied Computational Electromagnetics Society Symposium in China

作者： Yong Song Kaihong Song Xianliang Wu Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University

ISBN: (纸本)9781509043118

We present an inductive powering system of a two-turns loop antenna which includes external and implanted antennas each with a cylinder magnetic core. It can wirelessly transfer power to loop antenna of a compact plane structure in cortex. Compared to single antenna without magnetic core, it can achieve highly-efficient (24.8%) wireless power transferring to implanted loop antenna and reduce the SAR to 83.2%. By improving the link power transfer efficiency and decreasing the SAR, we optimize the antenna's structural parameter. Then, we propose different liquid of concentrations to simulate human skin, which is used for power transmission experiment at 100MHz.

关键词： Transmitting antennas Antenna measurements Magnetic cores Wireless communication Skin Dielectrics

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A light and discriminative deep networks for off-line handwritten Chinese character recognition

A light and discriminative deep networks for off-line handwr...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Shimeng Yang Fudong Nian Teng Li Key Laboratory of Intelligent Computing & Signal Processing Anhui University Hefei China

ISBN: (纸本)9781538629024

A practical method to improve the performance of off-line Handwritten Chinese Character Recognition (HCCR) was proposed in this paper. The center loss was used in face verification task to optimize intra-class distance. With the joint supervision of softmax loss and center loss, A light convolutional neural networks (CNNs) framework was trained for off-line HCCR which could optimize inter and intra difference simultaneously. Extensive experimental results on pubic dataset demonstrate that the performance of HCCR is improved significantly.

关键词： Character recognition Convolution Training Neural networks Databases Feature extraction Face

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Wide-band EM scattering from multiple dielectric targets above a dielectric rough sea surface based on maehly approximation

Wide-band EM scattering from multiple dielectric targets abo...

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International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications (MAPE)

作者： Ran Bao An Qi Wang Zhi Xiang Huang Key Laboratory of intelligent Computing & Signal Processing Anhui University Hefei China

In this paper an efficient method is proposed to simulate the wide-band electromagnetic (EM) scattering from multiple dielectric targets above a dielectric PM rough sea surface, which is based on Chebyshev series and Maehly approximation. Comparing with the traditional frequency domain algorithms, such as method of moments (MoM), the proposed algorithm is efficiently in reducing the computational time. Simulated results show that the Maehly approximation and the repeatedly simulations obtained by the MoM have achieved an excellent agreement both in horizontal and vertical polarization.

关键词： Method of moments Reflection coefficient Scattering Dielectrics Rough surfaces Surface roughness Sea surface

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A improved Prophet routing based on congestion level of nodes in DTN

A improved Prophet routing based on congestion level of node...

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2017 IEEE 第2届先进信息技术,电子与自动化控制国际会议(IAEAC 2017)

作者： Guizhu Wang Jian Tao Hui Zhang Dong Pan Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University

The Prophet routing algorithm is a multi-copy algorithm which is based on historical information and transitive probability,which selects the relay node with a high probability to encounter the destin

ISBN: (纸本)9781467389808

关键词： Delay Tolerant Networks Node's Congestion Level Prophet Relay Node Trunk Node DTNA gene Ganglia Routing algorithms Messages Obstetric Delivery disruption-tolerant network

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Direct quantification for coronary artery stenosis using multiview learning

arXiv

引用

arXiv 2019年

作者： Zhang, Dong Yang, Guang Zhao, Shu Zhang, Yanping Zhang, Heye Li, Shuo Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China School of Computer Science and Technology Anhui University Hefei China Cardiovascular Research Centre Royal Brompton Hospital LondonSW3 6NP United Kingdom National Heart & Lung Institute Imperial College London LondonSW7 2AZ United Kingdom School of Biomedical Engineering Sun Yat-Sen University Shenzhen China Western University LondonON Canada

The quantification of the coronary artery stenosis is of significant clinical importance in coronary artery diseases diagnosis and intervention treatment. It aims to quantify the morphological indices of the coronary artery lesions such as minimum lumen diameter, reference vessel diameter, lesion length and these indices are the reference of the interventional stent placement. In this study, we propose a direct multiview quantitative coronary angiography (DMQCA) model as an automatic clinical tool to quantify the coronary artery stenosis from X-ray coronary angiography images. The proposed DMQCA model consists of a multiview module with two attention mechanisms, a key-frame module and a regression module, to achieve direct accurate multiple-index estimation. The multi-view module comprehensively learns the spatio-temporal features of coronary arteries through a three-dimensional convolution. The attention mechanisms of each view focus on the subtle feature of the lesion region and capture the important context information. The keyframe module learns the subtle features of the stenosis through successive dilated residual blocks. The regression module finally generates the indice estimation from multiple features. We evaluate the proposed model over 2100 X-ray coronary angiography images collected from 105 subjects from two viewpoints. Compared to other direct quantification methods, our DMQCA model achieves more accurate quantification, enabling to provide a patient-specic assessment of coronary artery stenosis. Copyright © 2019, The Authors. All rights reserved.

关键词： Heart

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Counting extreme U1 matrices and characterizing quadratic doubly stochastic operators

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Frontiers of Mathematics in China 2016年第3期11卷 647-659页

作者： Quanbing ZHANG Shangjun YANG Key Laboratory of Intelligent Computing & Signal Processing Ministry of EducationAnhui University Hefei 230039 China

The U1 matrix and extreme U1 matrix were successfully used to study quadratic doubly stochastic operators by R. Ganikhodzhaev and F. Shahidi [Linear Algebra Appl., 2010, 432： 24-35], where a necessary condition for a U1 matrix to be extreme was given. S. Yang and C. Xu [Linear Algebra Appl., 2013, 438： 3905-3912] gave a necessary and sufficient condition for a symmetric nonnegative matrix to be an extreme U1 matrix and investigated the structure of extreme U1 matrices. In this paper, we count the number of the permutation equivalence classes of the n × n extreme U1 matrices and characterize the structure of the quadratic stochastic operators and the quadratic doubly stochastic operators.

关键词： Extreme U1 matrix quadratic doubly stochastic operator,majorized permutation similar irreducible matrix

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Metrics Reloaded: Recommendations for image analysis validation

arXiv

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arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette 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 Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab 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 Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

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Fast stochastic ordinal embedding with variance reduction and adaptive step size

arXiv

引用

arXiv 2019年

作者： Ma, Ke Zeng, Jinshan Xiong, Jiechao Xu, Qianqian Cao, Xiaochun Liu, Wei Yao, Yuan School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen518055 China School of Computer Information Engineering Jiangxi Normal University NanchangJiangxi330022 China Tencent AI Lab Shenzhen Guangdong China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen518055 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China Department of Mathematics and by courtesy Department of Computer Science and Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

—Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years. Most of the existing methods are based on semi-definite programming (SDP), which is generally time-consuming and degrades the scalability, especially confronting large-scale data. To overcome this challenge, we propose a stochastic algorithm called SVRG-SBB, which has the following features: i) achieving good scalability via dropping positive semi-definite (PSD) constraints as serving a fast algorithm, i.e., stochastic variance reduced gradient (SVRG) method, and ii) adaptive learning via introducing a new, adaptive step size called the stabilized Barzilai-Borwein (SBB) step size. Theoretically, under some natural assumptions, we show the O(T1 ) rate of convergence to a stationary point of the proposed algorithm, where T is the number of total iterations. Under the further Polyak-Lojasiewicz assumption, we can show the global linear convergence (i.e., exponentially fast converging to a global optimum) of the proposed algorithm. Numerous simulations and real-world data experiments are conducted to show the effectiveness of the proposed algorithm by comparing with the state-of-the-art methods, notably, much lower computational cost with good prediction performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

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