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Recurrent Multi-connection Fusion Network for Single image Deraining

Recurrent Multi-connection Fusion Network for Single Image D...

引用

IEEE visual communications and image processing (VCIP)

作者： Yuetong Liu Rui Zhang Yunfeng Zhang Yang Ning Xunxiang Yao Huijian Han Dept. Computer Science & Technology Shandong University of Finance and Economics Jinan China Dept. Computer Science & Technology Shandong Jianzhu University Jinan China

ISBN: (纸本)9781665475938

Single image deraining is an important problem in many computer vision tasks because rain streaks can severely degrade the image quality. Recently, deep convolution neural network (CNN) based single image deraining methods have been developed with encouraging performance. However, most of these algorithms are designed by stacking convolutional layers, which encounter obstacles in learning abstract feature representation effectively and can only obtain limited features in the local region. In this paper, we propose a recurrent multi-connection fusion network (RMCFN) to remove rain streaks from single images. Specifically, the RMCFN employs two key components and multiple connections to fully utilize and transfer features. Firstly, we use a multi-scale fusion memory block (MFMB) to exploit multi-scale features and obtain long-range dependencies, which is beneficial to feed useful information to a later stage. Moreover, to efficiently capture the informative features on the transmission, we fuse the features of different levels and employ a multi-connection manner to use the information within and between stages. Finally, we develop a dual attention enhancement block (DAEB) to explore the valuable channel and spatial components and only pass further useful features. Extensive experiments verify the superiority of our method in visual effect and quantitative results compared to the state-of-the-arts.

关键词： image quality Rain Fuses visual communication Stacking Neural networks Feature extraction

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Quality-Constant Per-Shot Encoding by Two-Pass Learning-based Rate Factor Prediction

Quality-Constant Per-Shot Encoding by Two-Pass Learning-base...

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IEEE visual communications and image processing (VCIP)

作者： Chunlei Cai Yi Wang Xiaobo Li Tianxiao Ye Bilibili Inc. Shanghai China

ISBN: (纸本)9781665475938

Providing quality-constant streams can simultaneously guarantee user experience and prevent wasting bit-rate. In this paper, we propose a novel deep learning based two-pass encoder parameter prediction framework to decide rate factor (RF), with which encoder can output streams with constant quality. In first-pass, an RF is predicted based on spatial-temporal and pre-coding features of video segment. Then video segment is encoded using the predicted RF and then its VMAF is measured. If first pass VMAF doesn't meet target quality, a second pass prediction is performed using another model, in where results of first pass is added to features. Experiments show the proposed method requires only 1.55 times encoding complexity on average, meanwhile the accuracy, that the compressed video's actual VMAF is within ±1 around the target VMAF, reaches 98.88%. Compared with average rate mode, this method can both improve visual Quality and save ~10% bit-rate, as shown in demos 1 1 Subjective comparison videos can be downloaded at https://***/drive/folders/1BJNZ5HssxFaKBcXMenEdioLJ77hnZ-vx?usp=sharing..

关键词： Radio frequency Deep learning visualization image coding visual communication Predictive models User experience

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Distinguishing Computer-Generated images from Photographic images: a Texture-Aware Deep Learning-Based Method

Distinguishing Computer-Generated Images from Photographic I...

引用

IEEE visual communications and image processing (VCIP)

作者： Zicheng Zhang Wei Sun Xiongkuo Min Tao Wang Wei Lu Guangtao Zhai Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China

ISBN: (纸本)9781665475938

With the rapid development of computer graphics and generative models, computers are capable of generating images containing non-existent objects and scenes. Moreover, the computer-generated (CG) images may be indistinguishable from photographic (PG) images due to the strong representation ability of neural network and huge advancement of 3D rendering technologies. The abuse of such CG images may bring potential risks for personal property and social stability. Therefore, in this paper, we propose a dual-stream neural network to extract features enhanced by texture information to deal with the CG and PG image classification task. First, the input images are first converted to texture maps using the rotation-invariant uniform local binary patterns. Then we employ an attention-based texture-aware feature enhancement module to fuse the features extracted from each stage of the dual-stream neural network. Finally, the features are pooled and regressed into the predicted results by fully connected layers. The experimental results show that the proposed method achieves the best performance among all three popular CG and PG classification databases. The ablation study and cross-database validation experiments further confirm the effectiveness and generalization ability of the proposed algorithm.

关键词： Three-dimensional displays Fuses Databases visual communication Computational modeling Neural networks Feature extraction

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ViEdge: An Edge-based Platform for Video Analytics Applications in Smart Estates

ViEdge: An Edge-based Platform for Video Analytics Applicati...

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International conference on Computer communications and Networks (ICCCN)

作者： Vishal Choudhary Rahul Aggarwal Hock Beng Lim Binbin Chen Center for Smart Systems (CS2) Singapore University of Technology and Design Singapore Information Systems Technology and Design Pillar (ISTD) Singapore University of Technology and Design Singapore

ISBN: (数字)9798350384611

ISBN: (纸本)9798350348439

Video analytics plays a crucial role in the development of smart estates and cities. Applications such as garbage dumping detection, lift monitoring, safety surveillance, etc. rely on video analytics, and require fast response time. Traditional cloud-based systems are ill-suited for these applications due to their limitations in handling large volume of video data with low latency. In contrast, the IoT-Edge-Cloud paradigm is better suited for such applications, but it presents challenges such as system heterogeneity, and resource allocation and orchestration. There is a need for an efficient platform for distributed video stream processing where resource orchestration and scalability aspects are tailored to smart estate *** this paper, we present ViEdge, an edge-based platform for video analytics applications in smart estates. It is highly adaptable and scalable, making it ideal for various deployments in such environments. Our implementation of ViEdge utilizes Kubernetes (K8s) for resource management and orchestration, and Apache Storm for distributed video stream processing. To study ViEdge’s customization capabilities, we evaluated its performance on a heterogeneous edge testbed. We observed increased latency in Apache Storm when integrated with Kubernetes, affecting overall application performance. However, by developing a heuristic-based scheduler, we demonstrate that ViEdge effectively reduces end-to-end latency and enhances frame processing rates.

关键词： Storms visual analytics Surveillance image edge detection Urban areas Streaming media Safety

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Face Super Resolution based on Contrastive Learning

Face Super Resolution based on Contrastive Learning

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IEEE visual communications and image processing (VCIP)

作者： Wenlin Zhang Sumei Li Liqin Huang Tianjin International Engineering Institute Tianjin University Tianjin China School of Electrical and Information Engineering Tianjin University Tianjin China College of Physics and Information Engineering Fuzhou University Fuzhou China

ISBN: (纸本)9781665475938

Face super resolution (FSR) is a sub-field of super resolution (SR), which is to reconstruct low resolution (LR) face image into high resolution (HR) face image. Recently, the FSR methods based on face prior have been proved to be effective in FSR on higher upscaling factors. However, existing prior guided methods mostly adopt supervised prior extraction models trained with labels. The performance of supervised prior extraction method mainly depends on the accuracy of label so that the implicit informations of data are not fully utilized. And in practical application, the label acquisition work is routine and laborious. Therefore, to solve these problems, this paper proposes a novel contrastive learning (CL) based FSR method, which is based on the iterative collaboration of image reconstruction network and contrastive learning network. In each iteration, the reconstruction network uses the priors generated by the contrastive learning network to assist the image reconstruction and generates higher-quality SR images. Then, the SR image will feed into contrastive learning network to obtain more accurate prior. In addition, a new contrastive learning constraint function is designed to extract the representation of the augmented facial image as a prior by analysing the principal component information of the image. Quantitative and qualitative experimental results show that the proposed method is superior to the most advanced FSR method in high-quality face images super resolution reconstruction.

关键词： image resolution visual communication Design methodology Collaboration Feature extraction Data mining Iterative methods

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ICME 2022 - IEEE International conference on Multimedia and Expo 2022, Proceedings

ICME 2022 - IEEE International Conference on Multimedia and ...

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2022 IEEE International conference on Multimedia and Expo, ICME 2022

ISBN: (纸本)9781665485630

The proceedings contain 377 papers. The topics discussed include: partial-to-partial point cloud registration based on multi-level semantic-structural cognition;noise-tolerant learning with silhouette coefficient for unsupervised person re-identification;disentangled visual representations for extreme human body video compression;quality-oriented feature regression for robust image similarity metric;salient object detection via bilateral feature fusion and score sorting attention mechanism;watermark-preserving keypoint enhancement for screen-shooting resilient watermarking;learning robust convolutional neural networks with relevant feature focusing via explanations;fusing acoustic and text emotional features for expressive speech synthesis;double attention network based on sparse sampling;learning to augment face presentation attack dataset via disentangled feature learning from limited spoof data;towards proper contrastive self-supervised learning strategies for music audio representation;just noticeable learning for unsupervised anomaly localization and detection;triple disentangling network for unsupervised domain adaptation;and multi-modal adversarial adaptive network for misinformation detection on social media.

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EMERGING THREAT of DEEP FAKE: HOW to IDENTIFY and PREVENT IT 6

EMERGING THREAT of DEEP FAKE: HOW to IDENTIFY and PREVENT IT

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6th International conference on Future Networks and Distributed Systems, ICFNDS 2022

作者： Taha, Murooj Amer Khudhair, Wijdan Mahood Khudhur, Ahmed Mahmood Mahmood, Omar Abdulkareem Hammadi, Yousif I. Al-Husseinawi, Riyam Shihab Ahmed Aziz, Ahmed Bilad Alrafidain University College Department of Computer Engineering Techniques Diyala Iraq Department of Communications Engineering College of Engineering University of Diyala 32001 Iraq Bilad Alrafidain University College Department of Medical Instruments Engineering Techniques Diyala Iraq International Business Management Department Tashkent State University of Economics Tashkent Uzbekistan

ISBN: (纸本)9781450399050

Although manipulations of visual and aural media have been around for as long as there have been media, the relatively recent introduction of deep fakes has marked a turning point in the creation of fake information. Deep fakes are automated methods that allow the creation of fake information that is becoming increasingly difficult for human observers to see. These procedures are made possible by the most recent technological advancements in artificial intelligence and deep learning. Deep Learning is a powerful method that is now being implemented in a variety of industries, including natural language processing, computer vision, image processing, and machine vision. Deep fakes are created by the use of deep learning algorithms in order to synthesize and modify photos, videos, or sounds of a person, to the point that human people are unable to discern the fake from the real one. In this article, we present a workable description of deep fakes as well as an outline of the technology that lies underneath them. In order to assist people in thinking about the future of deep fakes, we outline the benefits of the deepfake as well as the potential threats. This study presents a complete analysis of deep fake methods and discusses the most effective strategies for preventing counterfeiting. © 2022 ACM.

关键词： Computer vision

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1st International conference on Advanced Computing, Machine Learning, Robotics and Internet Technologies, AMRIT 2023

1st International Conference on Advanced Computing, Machine ...

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1st International conference on Advanced Computing, Machine Learning, Robotics and Internet Technologies, AMRIT 2023

ISBN: (纸本)9783031472237

The proceedings contain 46 papers. The special focus in this conference is on Advanced Computing, Machine Learning, Robotics and Internet Technologies. The topics include: Galactic Simulation: visual Perception of Anisotropic Dark Matter;Protocol Anomaly Detection in IIoT;Agricultural Informatics & ICT: The Foundation, Issues, Challenges and Possible Solutions—A Policy Work;a Guava Leaf Disease Identification Application;text to image Generation Using Attentional Generative Adversarial Network;attention-CoviNet: A Deep-Learning Approach to Classify Covid-19 Using Chest X-Rays;a Deep Learning Framework for Violence Detection in Videos Using Transfer Learning;multi-focus image Fusion Methods: A Review;cache Memory and On-Chip Cache Architecture: A Survey;authenticating Smartphone Users Continuously Even if the Smartphone is in the User’s Pocket;Comparative Analysis of Machine Learning Algorithms for COVID-19 Detection and Prediction;machine Learning Classifiers Explanations with Prototype Counterfactual;a Systematic Study of Super-Resolution Generative Adversarial Networks: Review;Stance Detection in Manipuri Editorial Article Using CRF;deep Learning Based Software Vulnerability Detection in Code Snippets and Tag Questions Using Convolutional Neural Networks;a Comprehensive Study of the Performances of Imbalanced Data Learning Methods with Different Optimization Techniques;Smart Parking System Using Arduino and IR Sensor;quMaDe: Quick Foreground Mask and Monocular Depth Data Generation;fine-Grained Air Quality with Deep Air Learning;enhancing Melanoma Skin Cancer Detection with Machine Learning and image processing Techniques;image processing Technique and SVM for Epizootic Ulcerative Syndrome Fish image Classification.

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1st International conference on Advanced Computing, Machine Learning, Robotics and Internet Technologies, AMRIT 2023

1st International Conference on Advanced Computing, Machine ...

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1st International conference on Advanced Computing, Machine Learning, Robotics and Internet Technologies, AMRIT 2023

ISBN: (纸本)9783031472206

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Enhanced image Deblurring with the Fusion of Generative Adversarial Networks and Transformer Models 1st

Enhanced Image Deblurring with the Fusion of Generative Adve...

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1st International conference on Innovation and Emerging Trends in Computing and Information Technologies, IETCIT 2024

作者： Ranjan, Arti Ravinder, M. New Delhi India Department of Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida India

ISBN: (纸本)9783031808418

In the domain of image processing, deblurring remains a challenging task, particularly in restoring high-fidelity details from blurred images. This paper introduces a novel approach that synergizes the strengths of Generative Adversarial Networks (GANs) and Transformer models to address this challenge. Our methodology leverages the generative capability of GANs to produce realistic and sharp images, while incorporating the Transformer’s prowess in capturing long-range dependencies and intricate details within images. This adversarial training regime is designed to refine the model’s ability to generate deblurred images that are not only accurate in detail but also high in perceptual quality. Through extensive experiments on various datasets, our model demonstrates superior performance in deblurring, outperforming existing state-of-the-art methods in terms of both objective metrics and subjective visual quality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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