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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

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GAN-based image deblurring: A comparison 2

GAN-based image deblurring: A comparison

引用

2nd IEEE International conference on Electrical Engineering, Big Data and algorithms, EEBDA 2023

作者： Feng, Qi Jiang, Liyan Ji, Yuyang Zhang, Ruihan University of Wisconsin-Madison Madison United States Shanghai Jiao Tong University UM-SJTU Joint Institute Shanghai China New York University New York United States Xi'An Jiaotong-Liverpool University Suzhou China

ISBN: (纸本)9781665462532

In recent years, image deblurring algorithms based on Generative Adversarial Network (GAN) have been proposed. The motion deblurring has been studied for some time. The multi-scale architecture of convolutional neural networks (CNN) can clearly recover blurred images, but the processing time is long. Generative Adversarial Networks (GAN) performs well in style conversion and can clearly recover blurred images under a non-multi-scale architecture. The authors reviewed the popular papers related to DeblurGAN, compared the algorithm structure of different papers, related data sets and the accuracy of experimental results, which helped GAN based image deblur field to summarize and summarize, and enabled researchers to better promote the development of related fields. The authors review six papers, introduce their research motivation, research methods and experimental results, and show the differences of different deblur algorithms. Some excellent algorithms can work flexibly with a wide backbone, balancing performance and efficiency. They run faster than simple DeblurGAN while maintaining near-state of the art results. © 2023 IEEE.

关键词： Generative adversarial networks

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A N-Dimensional Discrete Chaotic Map and Its Performance

A N-Dimensional Discrete Chaotic Map and Its Performance

引用

Natural Language processing (ICNLP), International conference on

作者： xin Gao xiumei Cai Chengmao Wu School of Automation Xi’an University of Post and Telecommunications Xi’an China School of Electronic Engineering Xi’an University of Post and Telecommunications Xi’an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

In order to solve the problems of small key space and simple chaotic behavior of low-dimensional chaotic systems in discrete domain, an N-dimensional discrete chaotic mapping system is proposed. An N-dimensional discrete chaotic system is obtained by coupling Chebyshev mapping with ICMIC mapping. Taking two-dimensional chaotic mapping as an example, the Lyapunov index, bifurcation graph, correlation and other properties of the discrete chaotic system are analyzed and applied to more classical image encryption algorithms. Experimental simulation results show that the N-dimensional discrete chaotic mapping system has larger key space, better chaotic behavior, and better security performance for image encryption algorithms.

关键词： Performance evaluation Correlation Sensitivity Simulation Information security Bifurcation Natural language processing Encryption Trajectory Indexes

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Augmented Reality System Based on Real-Time Object 6D Pose Estimation 2

Augmented Reality System Based on Real-Time Object 6D Pose E...

引用

2nd International conference on image processing and Media Computing, ICIPMC 2023

作者： Zhao, Yan Zhang, Shaobo Zhao, Wanqing Wei, Ying Peng, Jinye Northwest University School of Information Science and Technology Xi'an China

ISBN: (纸本)9798350326611

Augmented Reality (AR) refers to the perfect integration of virtual information with the real environment through computer technology, enhancing people's understanding of the real environment. Due to its excellent interactivity and realistic visual effects, it has received extensive attention from various fields. The effectiveness of augmented reality largely depends on whether virtual information can be accurately rendered in the real scene, with the most critical aspect being the 6D pose estimation of the target object in the real scene. Deep learning-based pose estimation methods have become indispensable technology in AR systems due to their high accuracy and wide applicability. Among existing pose estimation algorithms, YOLO6D has the advantages of good stability and fast speed. However, for AR applications that require interaction with the human eye, due to its complex network structure and large computational complexity, it still faces challenges in real-time performance and deployment on mobile devices. Therefore, this paper aims to achieve a mobile AR system based on real-time pose estimation, using the YOLO-6D pose estimation network as the foundation, and conducting research from the aspects of model compression and system design. By using model compression algorithms include channel pruning and knowledge distillation (KD), the computational and parameter complexity of the pose estimation model is reduced, and the network inference speed is improved. At the same time, the AR tasks are decomposed in the system design to alleviate the performance pressure during deployment on mobile devices, thus building a high-quality, low-latency mobile AR system. © 2023 IEEE.

关键词： Augmented reality

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Multi-Exposure Fusion Light Field image Quality Assessment Based on Object Detection 14

Multi-Exposure Fusion Light Field Image Quality Assessment B...

引用

14th IEEE International conference on Signal processing, Communications and Computing, ICSPCC 2024

作者： Yao, Keke Liao, Guanglong Jiang, Gangyi Chen, Yeyao Yu, Mei Ningbo University Faculty of Information Science and Engineering Ningbo China

ISBN: (纸本)9798350366556

The multi-exposure fusion algorithms can effectively improve the dynamic range of light field imaging, but distortion is inevitably introduced during the process. Therefore, it is crucial to construct an effective quality assessment method for multi-exposure fusion light field images (MEF-LFIs). Aiming at the significant artifact distortion phenomenon of MEF-LFIs and the limitations of the traditional dynamic region extraction methods, the MEF-LFI quality assessment method combined with object detection is proposed. Firstly, an object detection model is utilized to extract dynamic region masks of the original LFI sequences, and the artifact feature fusion module is constructed for artifact distortion. Subsequently, the MEF-LFIs are converted from the RGB color space to the HSV color space to measure color distortion. Then, the multi-stream feature extraction networks with attention mechanism are constructed to extract artifact, spatial and angular distortion features, respectively. Finally, a regression model is constructed to measure the visual quality of MEF-LFIs. Experimental results show that the proposed method outperforms many representative image quality assessment methods and maintains well consistency with the human visual perception. © 2024 IEEE.

关键词： image quality

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A Novel Approach to Malicious Code Detection Using CNN-BiLSTM and Feature Fusion 6

A Novel Approach to Malicious Code Detection Using CNN-BiLST...

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6th International conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2024

作者： Zhang, Lixia Liu, Tianxu Shen, Kaihui Chen, Cheng Xi’an XD Power Systems Co. Ltd. Xi'an China Hangzhou Research Institute Xidian University Hangzhou China Xidian Digital Technology Xi'an China Harbin Engineerning University Harbin China

ISBN: (纸本)9798331541699

With the rapid advancement of Internet technology, the threat of malware to computer systems and network security has intensified. Malware affects individual privacy and security and poses risks to critical infrastructures of enterprises and nations. The increasing quantity and complexity of malware, along with its concealment and diversity, challenge traditional detection techniques. Static detection methods struggle against variants and packed malware, while dynamic methods face high costs and risks that limit their application. Consequently, there is an urgent need for novel and efficient malware detection techniques to improve accuracy and robustness. This study first employs the minhash algorithm to convert binary files of malware into grayscale images, followed by the extraction of global and local texture features using GIST and LBP algorithms. Additionally, the study utilizes IDA Pro to decompile and extract opcode sequences, applying N-gram and tf-idf algorithms for feature vectorization. The fusion of these features enables the model to comprehensively capture the behavioral characteristics of malware. In terms of model construction, a CNN-BiLSTM fusion model is designed to simultaneously process image features and opcode sequences, enhancing classification performance. Experimental validation on multiple public datasets demonstrates that the proposed method significantly outperforms traditional detection techniques in terms of accuracy, recall, and F1 score, particularly in detecting variants and obfuscated malware with greater stability. The research presented in this paper offers new insights into the development of malware detection technologies, validating the effectiveness of feature and model fusion, and holds promising application prospects. © 2024 IEEE.

关键词： Critical infrastructures

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A review of intelligent vehicle motion planning algorithms 3

A review of intelligent vehicle motion planning algorithms

引用

3rd International conference on Advanced algorithms and Signal image processing, AASIP 2023

作者： Kong, Lingming Wang, Zhiyu xi, Ruixuan Shen, Liangyi Ji, Zhao Li, Kai Co. Ltd. Tianjin300300 China

ISBN: (纸本)9781510668522

With vehicles becoming a necessity for people's life and work, the vigorous development of intelligent vehicles is of great significance in today's society in order to reduce traffic accidents, lower operating costs and achieve environmentally friendly travel. Motion planning, as the functional core of intelligent driving systems, is crucial to the driving safety of intelligent vehicles. This paper introduces the development status of intelligent vehicles at home and abroad, studies the current research status of mainstream motion planning techniques, analyses the technical challenges encountered in motion planning for intelligent vehicles in urban roads, and illustrates the development goal of establishing efficient and reliable motion planning algorithms in complex scenarios. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Motion planning

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Automatic conversion system of digital music score image based on image recognition algorithm 3

Automatic conversion system of digital music score image bas...

引用

3rd International conference on Computer Science and Communication Technology, ICCSCT 2022

作者： Yao, Yi Xi'an Conservatory of Music Shaanxi Xi'an710000 China

ISBN: (纸本)9781510661240

The development of target detection and recognition algorithms in the field of image processing has promoted the development of automatic image conversion systems for digital musical scores and related algorithms. This was crucial for the development of note-recognition coding and the conversion of musical scores into readable digital formats. The purpose of this paper is to study the automatic conversion system of digital music score image based on image recognition algorithm. The deep learning technology based on computer vision is used for musical score image recognition, and a digital musical score image automatic conversion system is designed and built. Harmonic peak method and confidence method are introduced, and their shortcomings are analyzed. Combined with the advantages of these two algorithms, an improved normal harmonic method is proposed, and the detailed ideas and steps of the algorithm are given. And the extracted fundamental frequency and time information are converted into pitch and time value combined with the basic knowledge of music theory. The recognition accuracy rate of the normal harmonic method proposed in this paper reaches more than 92%, which not only greatly improves the recognition accuracy, but also retains the strong anti-noise characteristics of the traditional harmonic peak method. © 2022 SPIE.

关键词： image recognition

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An adaptive fusion of RGB channel features U-Net for retinal vessel segmentation

An adaptive fusion of RGB channel features U-Net for retinal...

引用

2024 International conference on image processing and Artificial Intelligence, ICIPAl 2024

作者： xi, Mingyuan Gong, Zikang Shanghai Maritime University 1550 Haigang Ave. Pudong New Area Shanghai200135 China

ISBN: (纸本)9781510681514

Accurate segmentation of retinal vessels in fundus color photos holds significant importance in diagnosing ophthalmic conditions. This process plays a crucial role in identifying and analyzing various eye diseases. However, the retinal vascular structure is highly complex, with imbalances in multiscale and foreground-background proportions, making automatic segmentation challenging. Studies have indicated that valuable information is contained in the three RGB channels of color retinal vessel images. Nevertheless, there has been no research to date on integrating the information from the RGB channels with U-shaped segmentation networks for training. Therefore, in this paper, we propose an Attention U-Net with adaptive fusion of RGB three-channel features. The selective channel attention fusion module can adaptively integrate RGB three-channel features, and the fused features are input into the U-shaped network for training. In addition, an Attention Gate module is added before each upsampling concatenation operation in the decoder stage of the U-Net network. This effectively highlights specific target areas while suppressing irrelevant regions of the input image. Experimental results demonstrate that our method outperforms the segmentation results of other algorithms in three datasets in recent years. © 2024 SPIE.

关键词： Diagnosis

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Two-dimensional Kapur Entropy Multilevel Thresholding Algorithm Based on Dynamic Programming and Spatial Contextual Information 19

Two-dimensional Kapur Entropy Multilevel Thresholding Algori...

引用

19th International conference on Natural Computation, Fuzzy systems and Knowledge Discovery, ICNC-FSKD 2023

作者： Li, Jinming Lei, Bo Xi'an University of Posts and Telecommunications School of Communications and Information Engineering Xi'an China

ISBN: (纸本)9798350304398

The Kapur entropy multilevel thresholding image segmentation algorithm based on the two-dimensional histogram does not consider the spatial contextual information of image pixels, and the algorithm's time complexity increases exponentially with the increase of the number of thresholds. To address the above problems, this paper proposes a 2D-Kapur entropy multilevel thresholding image segmentation algorithm based on an energy curve by using both the neighborhood information and spatial contextual information of pixels. To improve the computational efficiency of the multilevel thresholding segmentation algorithm, a dynamic programming computational framework of the 2D-Kapur entropy multilevel thresholding algorithm based on the energy curve is constructed. The performance of the proposed algorithm is compared with several different image thresholding algorithms on the noise and complex images using several evaluation indexes. The experimental results verify the effectiveness of the proposed algorithm. © 2023 IEEE.

关键词： Dynamic programming

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Integrating the dynamic optimization strategies of Adam and AdaGrad 13

Integrating the dynamic optimization strategies of Adam and ...

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13th International conference on Intelligent Control and Information processing, ICICIP 2025

作者： Huang, Zhixuan Luo, xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

Artificial intelligence (AI) has been a key research area since the 1950s, initially focused on using logic and reasoning to create systems that understand language, control robots, and offer expert advice. With the rise of big data and deep learning, AI has advanced in applications like recommendation systems, image recognition, and machine translation, primarily through optimizing loss functions in deep neural networks to improve accuracy and reduce training *** descent is the core optimization method but faces challenges like slow convergence and local minima. To overcome these, algorithms like Momentum, AdaGrad, RMSProp, Adadelta, Adam, and Nadam have been developed, introducing momentum and adaptive learning rates to accelerate convergence. This paper presents a new optimization algorithm that combines the strengths of Adam and AdaGrad, offering better adaptability to different learning rates. © 2025 IEEE.

关键词： Contrastive Learning

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