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NTIRE 2020 Challenge on Image and Video Deblurring

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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

Harmony-Search and Otsu based System for Coronavirus Disease (COVID-19) Detection using Lung CT Scan Images

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arXiv 2020年

作者： Rajinikanth, V. Dey, Nilanjan Raj, Alex Noel Joseph Hassanien, Aboul Ella Santosh, K.C. Raja, N. Sri Madhava Department of Electronics and Instrumentation Engineering St. Joseph’s College of Engineering Chennai600119 India Department of Information Technology Techno India College of Technology KolkataWest Bengal700156 India Key Laboratory of Digital Signal and Image Processing of Guangdong Province Department of Electronic Engineering College of Engineering Shantou University Shantou Guangdong China Faculty of Computers and Artificial Intelligence Cairo University Egypt Department of Computer Science University of South Dakota 414 E Clark St VermillionSD57069 United States

Pneumonia is one of the foremost lung diseases and untreated pneumonia will lead to serious threats for all age groups. The proposed work aims to extract and evaluate the Coronavirus disease (COVID-19) caused pneumonia infection in lung using CT scans. We propose an image-assisted system to extract COVID-19 infected sections from lung CT scans (coronal view). It includes following steps: (i) Threshold filter to extract the lung region by eliminating possible artifacts;(ii) Image enhancement using Harmony-Search-Optimization and Otsu thresholding;(iii) Image segmentation to extract infected region(s);and (iv) Region-of-interest (ROI) extraction (features) from binary image to compute level of severity. The features that are extracted from ROI are then employed to identify the pixel ratio between the lung and infection sections to identify infection level of severity. The primary objective of the tool is to assist the pulmonologist not only to detect but also to help plan treatment process. As a consequence, for mass screening processing, it will help prevent diagnostic burden. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

OpEn: Code Generation for Embedded Nonconvex Optimization *

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IFAC-PapersOnLine 2020年第2期53卷 6548-6554页

作者： Pantelis Sopasakis Emil Fresk Panagiotis Patrinos Queen’s University Belfast School of Electronics Electrical Engineering and Computer Science (EEECS) and Centre for Intelligent and Autonomous Manufacturing Systems (i-AMS) Ashby Building Stranmillis Road BT9 5AG Belfast Northern Ireland WideFind AB Aurorum 1C 977 75 Luleå Sweden KU Leuven Department of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems Signal Processing and Data Analytics & Optimization in Engineering (OPTEC) Kasteelpark Arenberg 10 3001 Leuven Belgium

We present Optimization Engine (OpEn): an open-source code generation framework for real-time embedded nonconvex optimization, which implements a novel numerical method. OpEn combines the proximal averaged Newton-type method for optimal control (PANOC) with the penalty and augmented Lagrangian methods to compute approximate stationary points of nonconvex problems. The proposed method involves very simple algebraic operations such as vector products, has a low memory footprint and exhibits very good convergence properties that allow the solution of nonconvex problems on embedded devices. OpEn’s core solver is written is Rust — a modern, high-performance, memory-safe and thread-safe systems programming language — while users can call it from Python, MATLAB, C, C++, ROS or over a TCP socket.

关键词： Embedded numerical optimization nonconvex optimization problems code generation model predictive control moving horizon estimation Rust Robot Operating System

Learning Global Brain Microstructure Maps Using Trainable Sparse Encoders

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Learning Global Brain Microstructure Maps Using Trainable Sp...

IEEE International Conference on Image processing

作者： J. Rafael-Patino M. Barakovic G. Girard A. Daducci J.-P. Thiran Signal Processing Lab (LTS5) École Polytechnique Fédérale de Lausanne Switzerland Radiology D. Centre Hospitalier Universitaire Vaudois and University of Lausanne Switzerland Computer Science Department University of Verona Italy

ISBN: (纸本)9781538662502;9781538662496

Diffusion-Weighted Magnetic Resonance Imaging is the only non-invasive technique available to infer the underlying brain tissue microstructure. Currently, one of the promising methods for microstructure imaging is signal modelling using convex formulation, e.g. using the COMMIT framework. Despite the benefits introduced with such framework, an important limitation is the long convergence time, making the method unappealing for clinical applications. In order to address this limitation, we propose to use a neural network to learn the sparse representation of the data and perform an end-to-end reconstruction of the microstructure estimates directly from the diffusion-weighted data. Our results show that the neural network can accurately estimate the microstructure maps, 4 orders of magnitude faster than the convex formulation.

关键词： Microstructure Estimation Artificial neural networks White matter Magnetic resonance imaging Biological neural networks Encoding

PICSOM experiments in TRECVID 2018

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PICSOM experiments in TRECVID 2018

2018 TREC Video Retrieval Evaluation, TRECVID 2018

作者： Sjöberg, Mats Tavakoli, Hamed R. Xu, Zhicun Mantecón, Héctor Laria Laaksonen, Jorma Department of Computer Science Aalto University School of Science P.O.Box 15400 AaltoFI-00076 Finland Department of Signal Processing and Acoustics Aalto University School of Electrical Engineering P.O.Box 12200 AaltoFI-00076 Finland

This year, the PicSOM group participated only in the Video to Text (VTT), Description Generation subtask. For our submitted runs we used either the MSR-VTT dataset only, or MS COCO and MSR-VTT jointly for training. We used LSTM recurrent neural networks to generate descriptions based on multi-modal features extracted from the videos. We submitted four runs: • PICSOM_1: uses ResNet features for initialising the LSTM generator, and object and scene-type detection features as persistent input to the generator which is trained on MS COCO + MSR-VTT, • PICSOM_2: uses ResNet and object detection features for initialisation, and is trained on MS COCO + MSR-VTT, this is the only run based on our new PyTorch codebase, • PICSOM_3: uses ResNet and video category features for initialisation, and trajectory and audio-visual embedding features for persistent features, trained on MSR-VTT only, • PICSOM_4: is the same as PICSOM_3 except that the audio-visual embedding feature has been replaced with audio class detection outputs. The most significant difference between our runs came from expanding the original MSR-VTT training dataset by including MS COCO, which contains images annotated with captions. Having a larger and more diverse training set seems to bring larger improvements to the performance measures than using more advanced features. This finding has been confirmed also by our post-submission experiments that we are still continuing. Copyright © TRECVID *** rights reserved.

关键词： Embeddings

AIROGS: ARTIFICIAL INTELLIGENCE FOR ROBUST GLAUCOMA SCREENING CHALLENGE

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arXiv 2023年

作者： De Vente, Coen Vermeer, Koenraad A. Jaccard, Nicolas Wang, He Sun, Hongyi Khader, Firas Truhn, Daniel Aimyshev, Temirgali Zhanibekuly, Yerkebulan Le, Tien-Dung Galdran, Adrian Ballester, Miguel Ángel González Carneiro, Gustavo Devika, R.G. Hrishikesh, P.S. Puthussery, Densen Liu, Hong Yang, Zekang Kondo, Satoshi Kasai, Satoshi Wang, Edward Durvasula, Ashritha Heras, Jónathan Zapata, Miguel Ángel Araújo, Teresa Aresta, Guilherme Bogunović, Hrvoje Arikan, Mustafa Lee, Yeong Chan Cho, Hyun Bin Choi, Yoon Ho Qayyum, Abdul Razzak, Imran Van Ginneken, Bram Lemij, Hans G. Sánchez, Clara I. Group Informatics Institute Universiteit Van Amsterdam Noord-Holland Amsterdam Netherlands The Department of Biomedical Engineering and Physics Amsterdam Umc Locatie Amc Noord-Holland Amsterdam Netherlands Department of Radiology and Nuclear Medicine Radboudumc Gelderland Nijmegen Netherlands The Rotterdam Ophthalmic Institute Rotterdam Eye Hospital Rotterdam Netherlands Project Orbis International Inc. New York United States The Peking Union Medical College Hospital Beijing China The Xuanwu Hospital Capital Medical University Beijing100053 China The Tsinghua University Beijing China The Department of Diagnostic and Interventional Radiology University Hospital Aachen Aachen Germany Cmc Technologies Llp Nur-Sultan Kazakhstan Kbc Belgium Universitat Pompeu Fabra Barcelona Spain The Australian Institute for Machine Learning Aiml University of Adelaide Australia Icrea Barcelona Spain The Centre for Vision Speech and Signal Processing University of Surrey United Kingdom The College of Engineering Trivandrum India Founding Minds Software India The Institute of Computing Technology Chinese Academy of Sciences China The Muroran Institute of Technology Japan The Niigata University of Health and Welfare Japan The Schulich School of Medicine and Dentistry University of Western Ontario London Canada The Department of Mathematics and Computer Science University of la Rioja Spain Hospital Vall Hebron Passeig Roser 126 Sant Cugat del Vallès Barcelona08195 Spain UPRetina Barcelona Spain The Christian Doppler Laboratory for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria The Institute of Ophthalmology University College London London United Kingdom The Research Institute for Future Medicine Samsung Medical Center Seoul Korea Republic of Sungkyunkwan University Samsung Medical Center Seoul Korea Republic of The Department of Artificial Intelligence and Informatics Mayo Clinic Jacks

The early detection of glaucoma is essential in preventing visual impairment. Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effective manner, making glaucoma screening more accessible. While AI models for glaucoma screening from CFPs have shown promising results in laboratory settings, their performance decreases significantly in realworld scenarios due to the presence of out-of-distribution and low-quality images. To address this issue, we propose the Artificial Intelligence for Robust Glaucoma Screening (AIROGS) challenge. This challenge includes a large dataset of around 113,000 images from about 60,000 patients and 500 different screening centers, and encourages the development of algorithms that are robust to ungradable and unexpected input data. We evaluated solutions from 14 teams in this paper, and found that the best teams performed similarly to a set of 20 expert ophthalmologists and optometrists. The highest-scoring team achieved an area under the receiver operating characteristic curve of 0.99 (95% CI: 0.98-0.99) for detecting ungradable images on-the-fly. Additionally, many of the algorithms showed robust performance when tested on three other publicly available datasets. These results demonstrate the feasibility of robust AI-enabled glaucoma screening. Copyright © 2023, The Authors. All rights reserved.

关键词： Diagnosis

Towards more accurate automatic sleep staging via deep transfer learning

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arXiv 2019年

作者： Phan, Huy Chén, Oliver Y. Koch, Philipp Lu, Zongqing McLoughlin, Ian Mertins, Alfred De Vos, Maarten School of Computing University of Kent Chatham Maritime United Kingdom Institute of Biomedical Engineering University of Oxford Oxford United Kingdom Institute for Signal Processing University of Lübeck Lübeck Germany Department of Computer Science Peking University Beijing China

Although large annotated sleep databases are publicly available, and might be used to train automated scoring algorithms, it might still be a challenge to develop an optimal algorithm for your personal sleep study, which might have few subjects or rely on a different recording setup. Both directly applying a learned algorithm or retraining the algorithm on your rather small database is suboptimal. And definitely state-of-the-art sleep staging algorithms based on deep neural networks demand a large amount of data to be trained. This work presents a deep transfer learning approach to overcome the channel mismatch problem and enable transferring knowledge from a large dataset to a small cohort for automatic sleep staging. We start from a generic end-to-end deep learning framework for sequence-to-sequence sleep staging and derive two networks adhering to this framework as a device for transfer learning. The networks are first trained in the source domain (i.e. the large database). The pretrained networks are then finetuned in the target domain, i.e. the small cohort, to complete knowledge transfer. We employ the Montreal Archive of Sleep Studies (MASS) database consisting of 200 subjects as the source domain and study deep transfer learning on four different target domains: the Sleep Cassette subset and the Sleep Telemetry subset of the Sleep-EDF Expanded database, the Surrey-cEEGGrid database, and the Surrey-PSG database. The target domains are purposely adopted to cover different degrees of channel mismatch to the source domain. Our experimental results show significant performance improvement on automatic sleep staging on the target domains achieved with the proposed deep transfer learning approach and we discuss the impact of various fine tuning approaches. Copyright © 2019, The Authors. All rights reserved.

关键词： Sleep research

Straggler mitigation with tiered gradient codes

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arXiv 2019年

作者： Sasi, Shanuja Lalitha, V. Aggarwal, Vaneet Sundar Rajan, B. Department of Electrical Communication Engineering Indian Institute of Science Bangalore 560012 India Purdue University Signal Processing and Communications Research Center IIIT Hyderabad School of Industrial Engineering School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States

Coding theoretic techniques have been proposed for synchronous Gradient Descent (GD) on multiple servers to mitigate stragglers. These techniques provide the flexibility that the job is complete when any k out of n servers finish their assigned tasks. The task size on each server is found based on the values of k and n. However, it is assumed that all the n jobs are started when the job is requested. In contrast, we assume a tiered system, where we start with n1≥k tasks, and on completion of c tasks, we start n2-n1more tasks. The aim is that as long as k servers can execute their tasks, the job gets completed. This paper exploits the flexibility that not all servers are started at the request time to obtain the achievable task sizes on each server. The task sizes are in general lower than starting all n2tasks at the request times thus helping achieve lower task sizes which helps to reduce both the job completion time and the total server utilization. Copyright © 2019, The Authors. All rights reserved.

关键词： Gradient methods

Gas Composition Recognition Based on Analyzing Acoustic Relaxation Absorption Spectra:Wavelet Decomposition and Support Vector Machine Classifier

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Gas Composition Recognition Based on Analyzing Acoustic Rela...

2018 2nd International Conference on Electrical Engineering and Automation（ICEEA2018）

作者： Yaqiong Jia Bin Yu Mingdi Du Xiaoli Wang Department of Electronics and Information Engineering Key Laboratory of Signal &Information Processing Hunan Institute of Technology Department of Computer and Information Science Key Laboratory of Signal & Information Processing Hunan Institute of Technology

Gas acoustic spectrum represents properties of acoustic propagation, which can distinguish gas compositions. However in few existing methods of gas-composition-unknown recognition, the approaches of programmatically processing the acoustic spectrum curves have not yet been presented. We propose a method for gas-composition-unknown recognition by analyzing gas acoustic relaxation absorption spectrum(GARAS) based on wavelet multi-resolution analysis(MRA) and multi-class support vector machine(SVM). Features of GARAS are extracted by wavelet MRA, and then selected to obtain a few feature coefficients which are utilized to train and test multi-class SVM. Simulation results show that the proposed method completely classifies four examples of gas mixtures, and that it recognizes the mixtures with the same and similar concentration or temperature. This method realizes the numerically extracting and programmatically processing the information of GARAS, and implements gas-composition-unknown sensing based on acoustic spectrums.

关键词： gas compositions recognition gas acoustic relaxation absorption spectrum wavelet multi-resolution analysis multi-class support vector machine