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Remote sensing image colorization using symmetrical multi-scale DCGAN in YUV color space

VISUAL COMPUTER 2021年第7期37卷 1707-1729页

作者： Wu, Min Jin, Xin Jiang, Qian Lee, Shin-jye Liang, Wentao Lin, Guo Yao, Shaowen Yunnan Univ Sch Software Kunming Yunnan Peoples R China Natl Chiao Tung Univ Inst Technol Management Hsinchu Peoples R China

Image colorization technique is used to colorize the gray-level image or single-channel image, which is a very significant and challenging task in image processing, especially the colorization of remote sensing images. This paper proposes a new method for coloring remote sensing images based on deep convolution generation adversarial network. The adopted generator model is a symmetrical structure using the principle of auto-encoder, and a multi-scale convolutional module is specially designed to introduce into the generator model. Thus, the proposed generator can enable the whole model to retain more image features in the process of up-sampling and down-sampling. Meanwhile, the discriminator uses residual neural network 18 that can compete with the generator, so that the generator and discriminator can effectively optimize each other. In the proposed method, the color space transformation technique is first utilized to convert remote sensing images from RGB to YUV. Then, the Y channel (a gray-level image) is used as the input of the neural network model to predict UV channels. Finally, the predicted UV channels are concatenated with the original Y channel as a whole YUV that is then transformed into RGB space to get the final color image. Experiments are conducted to test the performance of different image colorization methods, and the results show that the proposed method has good performance in both visual quality and objective indexes on the colorization of remote sensing image.

关键词： Image colorization Multi-scale convolutional Remote sensing image deep convolutional generative adversarial networks

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Robust unsupervised anomaly detection via multi-time scale DCGANs with forgetting mechanism for industrial multivariate time series

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NEUROCOMPUTING 2021年 423卷 444-462页

作者： Liang, Haoran Song, Lei Wang, Jianxing Guo, Lili Li, Xuzhi Liang, Ji Chinese Acad Sci Technol & Engn Ctr Space Utilizat Key Lab Space Utilizat Beijing 100094 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Tsinghua Univ Sch Software Beijing 100084 Peoples R China

Detecting anomalies in time series is a vital technique in a wide variety of industrial application in which sensors monitor expensive machinery. The complexity of this task increases when heterogeneous sensors provide information of different attributes, scales and characteristics from the same machine. Actually, the challenges of anomaly detection for industrial time series are to design effective pre-processing, feature extraction and overcome the lack of abnormal samples. Recent deep learning models have shown prominent abilities on raw multivariate time series, alleviating these previous works. In this work, a novel framework named multi-time scale deep convolutional generative adversarial network (MTS-DCGAN) is proposed to deal with anomaly detection of industrial time series. Firstly, multivariate time series are transformed into the multi-channel signature matrices via sliding window based cross correlation computation, and therein forgetting mechanism is introduced to effectively avoid false alarms due to excessive influence of old sequences. Then the framework conducts an unsupervised adversarial training of multi-channel signature matrices and capture their hidden features via deep convolution structure. Besides, a novel threshold setting strategy is proposed to optimize anomaly detection performance under imbalance of normal and abnormal data. Finally, the proposed framework is assessed against the experiments on four datasets. Within this study, experimental results show our framework outperforms the comparison algorithms in terms of model performance and robustness, providing an effective anomaly detection method for industrial multivariate time series. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Multivariate time series Unsupervised anomaly detection Multi-time scale deep convolutional generative adversarial networks Threshold setting strategy

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deep Learning Methods for Screening Pulmonary Tuberculosis Using Chest X-rays

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 2021年第1期9卷 39-49页

作者： Dasanayaka, Chirath Dissanayake, Maheshi Buddhinee Univ Peradeniya Fac Engn Dept Elect & Elect Engn Kandy Sri Lanka

Tuberculosis (TB) is a contagious bacterial airborne disease, and is one of the top 10 causes of death worldwide. According to the World Health Organisation, around 1.8 billion people are infected with TB and 1.6 million deaths were reported in 2018. More importantly, 95% of cases and deaths were from developing countries. Yet, TB is a completely curable disease through early diagnosis. To achieve this goal one of the key requirements is efficient utilisation of existing diagnostic technologies, among which chest X-ray is the first line of diagnostic tool used for screening for active TB. The presented deep learning pipeline consists of three different modern deep learning architectures, to generate, segment, and classify lung X-rays. Apart from this, image preprocessing, image augmentation, genetic algorithm based hyper parameter tuning, and model ensembling were used to improve the diagnostic process. We were able to achieve classification accuracy of 97.1% (Youden's index-0.941, sensitivity of 97.9%, specificity of 96.2%) which is a considerable improvement compared to the existing work in the literature. In our work, we present a highly accurate, automated TB screening system using chest X-rays, which would be helpful especially for low income countries with low access to qualified medical professionals.

关键词： Machine Learning automated Tuberculosis Screening Chest/Lung X-ray analysis deep convolutional generative adversarial networks segmentation and Classification

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Unveiling Spoofing Attempts: A DCGAN-based Approach to Enhance Face Spoof Detection in Biometric Authentication

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第4期15卷 959-969页

作者： Rao, Vuda Sreenivasa Kasireddy, Shirisha Mishra, Annapurna Salini, R. Godla, Sanjiv Rao Bedair, Khaled Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram Andhra Pradesh India JNTUH Vignana Bharathi Inst Technol Hyderabad India Silicon Inst Technol Elect & Commun Engn Bhubaneswar India Panimalar Engn Coll Dept CSE Chennai Tamil Nadu India Aditya Coll Engn & Technol Suraplem Dept AIML& Data Sci Suraplem Andhra Pradesh India Qatar Univ Coll Arts & Sci Dept Social Sci POB 2713 Doha Qatar

Face spoofing attacks have become more dangerous as biometric identification has become more widely used. Through the utilisation of false facial photographs, attackers seek to fool systems in these assaults, endangering the security of biometric authentication devices and perhaps allowing unauthorized access to private information. Effectively recognizing and thwarting such spoofing attacks is critical to the dependability and credibility of biometric identification systems in a variety of applications. This research seeks to offer a unique strategy that uses deep convolutional generative adversarial networks (DCGANs) to improve face spoof detection in order to counter the challenge provided by face spoofing assaults. In order to strengthen the security of biometric authentication systems in applications like identity verification, access control, and mobile device unlocking, the goal is to increase the accuracy and effectiveness of facial spoof detection. The training dataset is then supplemented with these artificial images, which strengthens the face spoof detection system's resilience. More accurate face spoofing is made possible by the strategy that leverages the discriminative characteristics obtained throughout the process to train the discriminator network employing adversarial learning to discriminate between actual and fake images. Experiments on the CelebFaces Attributes (CelebA) datasets show how effective the suggested method is over traditional techniques. The suggested technique outperforms conventional methods and achieves an astounding accuracy of 99.1% in face-spoof detection systems. The system exhibits impressive precision in differentiating between real and fake faces through the efficient use of artificial intelligence and adversarial learning. This effectively decreases the possibility of unwanted access and enhances the overall dependability of biometric authentication methods.

关键词： Biometric authentication systems deep convolutional generative adversarial networks face spoof detection synthetic image generation unauthorized access

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AI-based Robust Convex Relaxations for Supporting Diverse QoS in Next-Generation Wireless Systems 41

AI-based Robust Convex Relaxations for Supporting Diverse Qo...

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41st IEEE International Conference on Distributed Computing Systems (ICDCS)

作者： Chan, Steve Krunz, Marwan Griffin, Bob Vit Tall Orlando FL 32835 USA Univ Arizona Tucson AZ 85721 USA

ISBN: (纸本)9781665449328

Supporting diverse Quality of Service (QoS) requirements in 5G and beyond wireless systems often involves solving a succession of convex optimization problems, with varied approaches to optimally resolve each problem. Even when the input set is specifically designed/architected to segue to a convex paradigm, the resultant output set may still turn out to be nonconvex, thereby necessitating a transformation to a convex optimization problem via certain relaxation techniques. This transformation in itself may spawn yet other nonconvex optimization problems, highlighting the need/opportunity to utilize a Robust Convex Relaxation (RCR) framework. In this paper, we explore a particular class of convolutional Neural networks (CNNs), namely deep convolutional generative adversarial Network (DCGANs), to solve not only the QoS-related convex optimization problems but also to leverage the same RCR mechanism for tuning its own hyperparameters. This approach gives rise to various technical challenges. For example, Particle Swarm Optimization (PSO) is often used for hyperparameter reduction/tuning. When implemented on a DCGAN, PSO requires converting continuous/discontinuous hyperparameters to discrete values, which may result in premature stagnation of particles at local optima. The involved implementation mechanics, such as increasing the inertial weighting, may spawn yet other convex optimization problems. We introduce a RCR framework that capitalizes upon the feed-forward structure of the "You Only Look Once" (YOLO)-based DCGAN. Specifically, we use a squeezed deep convolutional-YOLO-generative adversarial Network (DC-YOLO-GAN), hereinafter referred to as a Modified Squeezed YOLO v3 Implementation (MSY3I), combined with convex relaxation adversarial training to improve the bound tightening for each successive neural network layer and to better facilitate the global optimization via a specific numerical stability implementation within MSY3I.

关键词： Quality of Service 5G networks Nonconvex Optimization Convex Relaxation Particle Swarm Optimization deep convolutional generative adversarial networks Numerical Implementation You Only Look Once Mixed Integer Non-linear Programming Robust Convex Relaxation Framework

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A Co-Attention Method Based on generative adversarial networks for Multi-view Images 22

A Co-Attention Method Based on Generative Adversarial Networ...

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22nd IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Fall)

作者： Huang, Qi-Xian Shi, Shu-Pei Lin, Guo-Shiang Shen, Day-Fann Sun, Hung-Min Natl Tsing Hua Univ Inst Informat Syst & Applicat Hsinchu Taiwan Natl Yunlin Univ Technol Dept Elect Engn Touliu Yunlin Taiwan Natl Chin Yi Univ Technol Dept Comp Sci & Informat Engn Taichung Taiwan Natl Tsing Hua Univ Dept Comp Sci Hsinchu Taiwan

ISBN: (纸本)9781665404037

In this paper, we use deep convolutional generative adversarial networks (DCGANs) method to generate more images with multiple views to increase our dataset diversity. We use 3D-model different views for training DCGAN to make interpolation between the leftest and lightest random vectors, which means it can generate leftest to lightest images. After producing many of multi-view images, we combine with CNN based modules called co-attention map generator to look for common features of the same class but in different views clothing. By applying the learned generator to all images, the corresponding co-attention maps are obtained. we can fluently apply the proposed method can function well for multi-view objects on different types of clothing classes.

关键词： Multi-view clothing co-attention map deep convolutional generative adversarial networks

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Research on Radar Target Recognition Method Based on deep Learning

Research on Radar Target Recognition Method Based on Deep Le...

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International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV)

作者： Shi, Duanyang Lin, Qiang Hu, Bing Wang, Guochao Air Force Early Warning Acad Wuhan Peoples R China 95174 PLA Troops Wuhan Wuhan Peoples R China

ISBN: (数字)9781510651838

ISBN: (纸本)9781510651838;9781510651821

At present, after radar target detection, radar target recognition mainly depends on manual judgment. Manual identification relies too much on the operator's personal experience and subjective consciousness, which takes a long time and has a large error. To solve this problem, a radar target recognition method based on deep learning is proposed. By analyzing the differential characteristics of radar echoes of different targets, taking the range profile of radar echo sequence as the data set, a deep residual network classifier model is designed to classify and identify the types of radar targets. In order to improve the generalization ability of the network and avoid the over-fitting problem, the deep convolutional generative adversarial network is used to expand the range profile data set of radar echo sequence. In order to ensure the quality of the generated samples during data set expansion, the expanded samples are screened with the peak signal-to-noise ratio as the index. The experimental results of radar measured data show that this method has a good effect on radar target recognition.

关键词： deep learning radar target recognition deep convolutional generative adversarial networks deep residual network

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A Novel Image-Based Diagnosis Method Using Improved DCGAN for Rotating Machinery

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SENSORS 2022年第19期22卷 7534-7534页

作者： Gao, Yangde Piltan, Farzin Kim, Jong-Myon Univ Ulsan Dept Elect Elect & Comp Engn Ulsan 44610 South Korea PD Technol Cooperat Ulsan 44610 South Korea

Rotating machinery plays an important role in industrial systems, and faults in the machinery may damage the system health. A novel image-based diagnosis method using improved deep convolutional generative adversarial networks (DCGAN) is proposed for the feature recognition and fault classification of rotating machinery. First, vibration signal data from the rotating machinery is transformed into time-frequency feature 2-D image data by a continuous wavelet transform and used for fault classification with the neural network method. The adaptive deep convolution neural network (ADCNN) is then combined with the generative adversarial networks (GANs) to improve the performance of the feature self-learning ability from input data. Compared with different fault diagnosis methods, the proposed method has better performance for image feature classification in rotating machinery.

关键词： rotating machinery fault classification deep convolutional generative adversarial networks

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generative adversarial networks-Based Synthetic Microstructures for Data-Driven Materials Design

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ADVANCED THEORY AND SIMULATIONS 2022年第5期5卷

作者： Narikawa, Ryuichi Fukatsu, Yoshihito Wang, Zhi-Lei Ogawa, Toshio Adachi, Yoshitaka Tanaka, Yuji Ishikawa, Shin Nagoya Univ Dept Mat Sci & Engn Chikusa Ku Furo Cho Nagoya Aichi 4648601 Japan JFE Steel Steel Res Lab Cyuo Ku 1 Kawasaki Cho Chiba 2600835 Japan

To understand the material paradigm, data-driven material design necessitates both microstructural input and output in the form of visual images. Therefore, generative adversarial networks (GAN)-based deep convolutional GAN, cycle-consistent GAN, and super-resolution GAN techniques are used to generate, translate, and improve the quality of microstructural images in this study. The reconstructed virtual microstructural images are realistic and indistinguishable from the real ones. Furthermore, using GAN techniques to reconstruct microstructural image suggests promising ways to design desired microstructures using parameterized descriptors and image augmentation, which are expected to advance data-driven materials research.

关键词： cycle generative adversarial networks data-driven material design deep convolutional generative adversarial networks microstructural-image reconstruction super-resolution generative adversarial networks

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OpenNIG - Open Neural Image Generator 13

OpenNIG - Open Neural Image Generator

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13th International Conference on Communications (COMM)

作者： Avram, Andrei-Marius Morogan, Luciana Toma, Stefan-Adrian Univ Politehn Bucuresti UPB Bucharest Romania Mil Tech Acad Ferdinand I MTA Bucharest Romania

ISBN: (纸本)9781728156118

generative models are statistical models that learn a true underlying data distribution from samples using unsupervised learning, aiming to generate new data points with some variation. In this paper, we introduce OpenNIG (Open Neural Image Generator), an open-source neural networks toolkit for image generation. It offers the possibility to easily train, validate and test state of the art models. The framework also contains a module that enables the user to directly download and process some of the most common databases used in deep learning. OpenNIG is freely available via GitHub.

关键词： image generation deep convolutional generative adversarial networks deep convolutional variational autoencoder generative models

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