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32nd International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2024

作者： Draganic, Andela Stankovic, Dorde Lekic, Nedeljko Ioana, Cornel Orovic, Irena University of Montenegro Faculty of Electrical Engineering Podgorica Montenegro Podgorica Montenegro Isére Grenoble France Grenoble Institute of Technology GIPSA-lab Is re Grenoble France Podgorica Montenegro Lisbon1700-097 Portugal

ISBN: (纸本)9789532901382

The paper introduces an invisible image watermarking approach using a binary image as a watermark in combination with the Hermite transform. By using the secret key, the binary watermark is separated into several partial binary images before being embedded into the transform domain coefficients. Embedding is done into several bit planes of the Hermite coefficients to ensure invisibility and robustness. The Hermite transform proves to be efficient in diverse applications and outperforms traditional approaches, especially in block-based image processing. Namely, it is shown that the Hermite transform can be used for image deblocking to mitigate the impact of ringing artifacts and blurring introduced by using the Fourier or discrete cosine transform-based approaches. Our focus is on defining a low-complexity architecture for real-time Hermite-based image watermarking. Therefore, the procedure is adopted to work in a vector manner to facilitate the hardware implementation. The schematic depiction of the design is provided and the complexity is discussed. © 2024 University of Split, FESB.

关键词： Binary images

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Retargeted broad learning systems for image classification

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digital SIGNAL processing 2025年 159卷

作者： Jin, Junwei Zhu, Xianzheng Geng, Yun Liu, Jiahang Li, Yanting Liang, Jing Chen, C. L. Philip Li, Peng Henan Univ Technol Key Lab Grain Informat Proc & Control Minist Educ Zhengzhou Peoples R China Henan Univ Technol Henan Key Lab Grain Storage Informat Intelligent P Zhengzhou Peoples R China Henan Univ Technol Sch Artificial Intelligence & Big Data Zhengzhou 450001 Peoples R China Henan Univ Technol Inst Complex Sci Zhengzhou 450001 Peoples R China Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China Zhengzhou Univ Light Ind Coll Comp Sci & Technol Zhengzhou 450001 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510641 Peoples R China

The Broad Learning System (BLS) is recognized for its adept balance between efficiency and accuracy, displaying notable performance in image classification tasks owing to its streamlined network architecture and effective learning methodology. However, it faces significant challenges due to two prominent deficiencies that notably impede its learning efficacy. Firstly, the rigid binary labeling strategy inherent in BLS-based models imposes constraints on the model's adaptability. Additionally, the resultant broad features often exhibit redundancy, posing a risk of incorporating extraneous features. To address these issues, this article proposes three refined BLS-based models. Initially, a retargeting methodology is integrated into the standard BLS framework to alleviate constraints on regression targets, introducing the 82-based retargeted BLS (L2ReBLS) model. Subsequently, to mitigate the adverse effects of redundant features, the 82,1 regularizer is adopted as a replacement for the Frobenius norm in feature selection, resulting in the L21ReBLS model. Furthermore, the projection matrix of BLS is concurrently constrained with 82 and 82,1 regularization method simultaneously. Efficient iterative optimization methodologies via the alternating direction method of multipliers are devised for the purpose of solving the proposed approaches. Ultimately, comprehensive experiments conducted on diverse image databases are to highlight the superior performance of our proposed approaches in comparison to other state-of-the-art classification algorithms.

关键词： Broad learning system Retargeted label space Sparse regularization Optimization image classification

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Estimation Analysis of Edge and Line Detection Methods in digital image processing

Estimation Analysis of Edge and Line Detection Methods in Di...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Prateek Garg Rakesh Kumar Yadav Devaraj Verma C D. Nirmala Nilesh P. Sable Krishn Murari Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar Karnataka Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Information Technology Pune India Department of Civil Engineering Vivekananda Global University Jaipur India

This paper provides an estimation evaluation of the area and line detection methods in virtual photo processing. This analysis evaluates the performance of numerous facet and line detection algorithms in phrases of facet/line first-rate, accuracy, computational complexity, and robustness to noise in the context of artificial and actualinternational snapshots. Mainly, it discusses the overall performance of 4 edge detection methods: Roberts, Prewitt, Sobel, and Canny; and 3 line detection techniques: Hough remodel, Linear Estimation, and Probabilistic Hough remodel. as compared to the classical algorithms, the Probabilistic Hough rework algorithm is determined to have the excellent accuracy and robustness to noise. Furthermore, a contrast of computational complexities shows that the Hough transform has the bottom complexity and computational time, even as the Linear Estimation algorithm has the best complexity and computational time. The primary outcome of this study is that the brink/line first-rate, accuracy, computational complexity, and robustness are very dependent on the sort of entered snapshots and the respective selected parameters.

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Algorithm Analysis of 3D Visualization image Fusion Based on Intelligent digital Media Technology

Algorithm Analysis of 3D Visualization Image Fusion Based on...

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2023 International Conference on Data Science and Network Security, ICDSNS 2023

作者： Zhang, Xinyu Cardiff University Cardiff United Kingdom

ISBN: (纸本)9798350301595

3D visualization image fusion technology is one of the key technologies in digital media technology, which can fuse images collected by different devices using digital media technology to gradually reveal the objects in the images. This article provides the process of 3D visualization image fusion, including several steps such as image preprocessing. image fusion, effect evaluation, and subsequent processing. This article elaborates on the basic principles of three-dimensional wavelet transform and its application in image fusion. Practice has shown that in terms of visual image fusion, the 3D wavelet transform algorithm is superior to the NSCT algorithm. 3D visualization image fusion technology can store more critical scenes with high accuracy, laying a solid foundation for the smooth progress of subsequent related work. This article will comprehensively analyze the process and algorithms of image fusion based on the specific concepts of image fusion, and summarize the performance evaluation of image fusion algorithms for reference. © 2023 IEEE.

关键词： image fusion

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Neural processor microarchitecture design for CNN processing.acceleration 11

Neural processor microarchitecture design for CNN processing...

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Optoelectronic Imaging and Multimedia Technology XI 2024

作者： Tabunshchik, Sergei Bykovskii, Sergei Kustarev, Pavel Faculty of Software Engineering and Computer Systems ITMO University 49 Kronverksky Pr. St. Petersburg197101 Russia

ISBN: (纸本)9781510682061

Mixed reality systems that integrate the real and digital worlds have recently gained popularity. The development of mixed reality devices in the form of wearable devices has been a significant trend in recent years. Key challenges in designing wearable mixed reality devices include reducing power consumption, enhancing productivity, and minimizing the size of the device. To address these challenges, neural processors are being incorporated into mixed reality systems. These are specialized hardware accelerators for convolutional neural network (CNN) processing. The use of neural processors is due to the fact that most environmental analysis and visualizations in mixed reality rely on CNNs. The use of these energy-efficient, high-performance processing.devices enables increased comfort in using the wearable device through the high-performance processing.of environmental data on a compact device. The paper is devoted to the design of the microarchitecture of a neural processor for hardware acceleration of CNN processing.based on the author's architecture of processor. The paper presents various microarchitectural solutions that can be used to accelerate CNN processing. We explore methods to optimize hardware resources and reduce time required for CNN processing. To achieve high throughput in pipelined computation, different algorithms for convolution calculations in a systolic array are examined. Based on the results of this research, we provide estimates of the characteristics of the neural processor with the proposed microarchitecture. © 2024 SPIE.

关键词： Convolutional neural networks

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A UAV-Based Remote Sensing image Automatic Monitoring System Empowered by Artificial Intelligence

A UAV-Based Remote Sensing Image Automatic Monitoring System...

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2024 International Conference on Intelligent algorithms for Computational Intelligence Systems, IACIS 2024

作者： Liu, Tao Zhang, Bohan College of Computer Science and Technology Heilongjiang Institute of Technology Harbin China School of Information Engineering China University of Geosciences Wuhan Wuhan China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China

ISBN: (纸本)9798350360660

In this study, an automatic monitoring system based on convolutional neural network (CNN) is proposed to address the automation and accuracy of remote sensing image processing. With the maturity and wide application of remote sensing and UAV technologies, efficient and accurate image analysis methods have become particularly important. We developed this system through in-depth research on multiple stages of data preprocessing. feature extraction and model training. In the experimental stage, through the comparison experiments with Support Vector Machine (SVM) and Random Forest (RF) models, it is found that the CNN model has significant advantages in processing.speed, anti-interference ability and accuracy. Especially in the processing.of urban remote sensing images, CNN exhibits up to 90% accuracy, showing its wide applicability and excellent cross-domain application capability. Overall, this study not only successfully develops an efficient and accurate automatic monitoring system for remote sensing images, but also provides strong theoretical and experimental support for future optimization of CNN architecture in natural environments, improvement of real-time data processing.capability, and extension to practical applications such as disaster monitoring and environmental protection. © 2024 IEEE.

关键词： Environmental monitoring

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DH-GAN: a physics-driven untrained generative adversarial network for holographic imaging

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OPTICS EXPRESS 2023年第6期31卷 10114-10135页

作者： Chen, Xiwen Wang, Hao Razi, Abolfazl Kozicki, Michael Mann, Christopher Clemson Univ Sch Comp 821 McMillan Rd Clemson SC 29631 USA Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85287 USA No Arizona Univ Dept Appl Phys & Mat Sci 1900 S Knoles Dr Flagstaff AZ 86011 USA

digital holography is a 3D imaging technique by emitting a laser beam with a plane wavefront to an object and measuring the intensity of the diffracted waveform, called holograms. The object's 3D shape can be obtained by numerical analysis of the captured holograms and recovering the incurred phase. Recently, deep learning (DL) methods have been used for more accurate holographic processing. However, most supervised methods require large datasets to train the model, which is rarely available in most DH applications due to the scarcity of samples or privacy concerns. A few one-shot DL-based recovery methods exist with no reliance on large datasets of paired images. Still, most of these methods often neglect the underlying physics law that governs wave propagation. These methods offer a black-box operation, which is not explainable, generalizable, and transferrable to other samples and applications. In this work, we propose a new DL architecture based on generative adversarial networks that uses a discriminative network for realizing a semantic measure for reconstruction quality while using a generative network as a function approximator to model the inverse of hologram formation. We impose smoothness on the background part of the recovered image using a progressive masking module powered by simulated annealing to enhance the reconstruction quality. The proposed method exhibits high transferability to similar samples, which facilitates its fast deployment in time-sensitive applications without the need for retraining the network from scratch. The results show a considerable improvement to competitor methods in reconstruction quality (about 5 dB PSNR gain) and robustness to noise (about 50% reduction in PSNR vs noise increase rate). (c) 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

关键词： digital holographic imaging image processing Numerical analysis Phase recovery Three dimensional imaging Wave propagation

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A comparative analysis of fault detection and process diagnosis methods based on a signal processing.paradigm

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DISCOVER APPLIED SCIENCES 2024年第1期7卷 1-22页

作者： Aiordachioaie, Dorel Dunarea de Jos Univ Galati 47 Domneasca St Galati 800008 Romania

An important paradigm in industrial engineering for fault detection and diagnosis purposes is signal processing. The various methods consider methods in the time, frequency, or time-frequency domain for signal processing.as state and output signals from the considered process. The objective of this work is to perform a comparative analysis of the most used methods based on a signal processing.paradigm and in the context of fault detection and process diagnosis. The electromechanical equipment that generates mechanical vibrations-as an effect of bearing faults-is considered and analyzed. The recorded data are explored with smaller and sliding frames, adapted to the processing.criteria used. Seven methods are considered for evaluation: two in the time domain, two in the frequency domain and three in the time-frequency domain. The main problem is to extract and select the right features to use in the classification stage. The methods of the time domain are based on statistical moments and signal modeling. The methods in the frequency domain use either the discrete components of power spectra or the features of the frequency domain. In the time-frequency domain, the coefficients of the time-frequency transforms define digital images, which are further processed. For testing, the methods are evaluated with real recorded data from bearings with several types and sizes of faults, i.e., incipient, medium, advanced, and large. Finally, the considered methods are compared from the point of view of five criteria, namely, the recognition rate, window length, response time, computational resources, and complexity of the algorithms. A global quality criterion is built and used to assess the quality of the methods. The results of the computer-based experiments show acceptable performance for all methods for the test case of bearings but the potential to detect more complex faults and change detection in the behavior of the machines, in general. Time-frequency methods offer an opti

关键词： Change detection Fault detection Signal processing Statistical moments Power spectra Time-frequency transform image processing Feature extraction Pattern recognition

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A Comprehensive Survey of image Steganography 2

A Comprehensive Survey of Image Steganography

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2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS)

作者： Kalaiarasi, G. Sudharani, B. Jonnalagadda, Sharon Christiana Battula, Harsha Vardhan Sanagala, Bhavana Vignans Fdn Sci Technol & Res Dept ACSE Guntur Andhra Pradesh India

ISBN: (纸本)9798350391558;9798350379990

This research study analyzes the multidimensional landscape of steganography, examining its historical roots, theoretical background, contemporary approaches, and various applications. Beginning with a historical overview, this study investigates the evolution of steganography from its ancient roots to its present iterations in the digital world. Next, the study progresses towards analyzing the fundamental principles and theoretical frameworks that underpin steganographic systems, such as cryptography and digital signal processing. Finally, this study presents a thorough evaluation of contemporary steganographic technologies, which range from simple LSB (Least Significant Bit) substitution techniques to advanced adaptive algorithms and machine learning methods by including deep-learning based steganography and coverless steganography. Notably, this study identifies key challenges, including detection resistance, payload capacity, and robustness against attacks. Overall, this study presents a thorough understanding of steganography, emphasizing its significance as a versatile tool for communication in the digital era, while also highlighting the challenges that pave way for future innovations.

关键词： image steganography Information hiding Convolutional Neural Network Steganography (CNN) Generative Adversarial Network Steganography (GAN) Stochastic computing

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Real-World image Reflection Removal: An Ultra-High-Definition Dataset and an Efficient Baseline

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2025年第5期35卷 4397-4408页

作者： Chen, Guang-Yong Zheng, Chao-Wei Fan, Guodong Su, Jian-Nan Gan, Min Philip Chen, C. L. Fuzhou Univ Coll Comp & Data Sci Fuzhou 350116 Peoples R China Minist Educ Fujian Key Lab Network Comp & Intelligent Informat Key Lab Intelligent Metro Univ Fujian Fuzhou 350108 Peoples R China Qingdao Univ Coll Comp Sci & Technol Qingdao 266071 Peoples R China Putian Univ New Engn Ind Coll Putian 351100 Fujian Peoples R China Putian Univ Putian Elect Informat Ind Technol Res Inst Putian 351100 Fujian Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510641 Peoples R China Minist Educ Engn Res Ctr Big Data Intelligence Fuzhou 350108 Peoples R China

Reflection removal is a crucial issue in image reconstruction, especially for high-definition images. Removing undesirable reflections can greatly enhance the performance of various visual systems, such as medical imaging, autonomous driving, and security surveillance. However, the resolution of existing reflection removal datasets is not high and the training data heavily relies on synthetic data, which hampers the performance of reflection removal methods and restricts the development of effective techniques tailored for high-definition images. Therefore, this paper introduces a new dataset, Real-world Reflection Removal in 4K (RR4K). This novel dataset, with its large capacity and high resolution of 6000x 4000 pixels, represents a significant advancement in the field, ensuring a realistic and high quality benchmark. Furthermore, building upon the dataset, we propose an efficient method for single-image reflection removal, optimized for high-definition processing. This method employs the U-Net architecture, enhanced with large kernel distillation and scale-aware features, enabling it to effectively handle complex reflection scenarios while reducing computational demands. Comprehensive testing on the RR4K dataset and existing low-resolution datasets has demonstrated the method's superior efficiency and effectiveness. We believe that our constructed RR4K dataset can better evaluate and design algorithms for removing undesirable reflection from real-world high-definition images. Our dataset and code are available at https://***/jengchauwei/RR4K.

关键词： Reflection image resolution Feature extraction Photography Kernel image reconstruction Glass Deep learning Circuits and systems Benchmark testing Single-image reflection removal image reconstruction benchmark dataset

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