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Range Extension for Inpainting Using image Prediction based on Non-Harmonic Analysis 10

Range Extension for Inpainting Using Image Prediction Based ...

引用

10th international conference on systems and Informatics, ICSAI 2024

作者： Du, Yuhang Hasegawa, Masaya Hirobayashi, Shigeki Graduate School of Science and Engineering University of Toyama Toyama9308555 Japan

ISBN: (纸本)9798331510138

This paper introduces a novel scratch image restoration method leveraging non-harmonic analysis to predict and reconstruct damaged regions caused by scratches. Traditional techniques, such as the 2D discrete cosine transform and 2D fast Fourier transform, often rely on harmonic components and periodic assumptions, limiting their ability to handle the complex, non-periodic patterns typical of scratch damage. In contrast, our method identifies and uses non-harmonic components, dynamically adapting to local image features. This approach enhances reconstruction accuracy, particularly for scratched images with intricate structures and non-repetitive textures. Experimental results demonstrate that the proposed method significantly outperforms conventional techniques, achieving average improvements of approximately 10.32 dB in peak signal-to-noise ratio and 0.3795 in structural similarity index measure over 2D discrete cosine transform, and 9.44 dB in peak signal-to-noise ratio and 0.2433 in structural similarity index measure over 2D fast Fourier transform. Our method excels in repairing scratch-affected regions, especially in areas with repetitive structures and distinct characteristics. Our approach provides a promising solution for accurate and reliable scratch image restoration. © 2024 ieee.

关键词： Restoration

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Research on an End-to-End Assistive Model for the Visually Impaired Population 5

Research on an End-to-End Assistive Model for the Visually I...

引用

5th international conference on Computer Vision, image and Deep Learning, CVIDL 2024

作者： Kuang, Zhenbang Ma, Chuangfeng Wang, Junguo Zhao, Zhihong Zhou, Youyu Beijing Institute of Technology Zhuhai School of Applied Science and Civil Engineering Zhuhai China

ISBN: (纸本)9798350373820

Vision is one of the most important sensory organs for humans, which it helps us better understand and perceive the world. However, for the visually impaired population, the weakness of this sensory organ brings additional challenges to their lives. To help the visually impaired community gain a new 'vision', this paper proposes the establishment of an end-to-end visual question answering model (MultiCLIPQA) based on CLIP. And use the CLIP model to extract features from the VizWiz dataset, we further accomplish the task of visual question answering. A comparative study was conducted on four different encoders within the CLIP model. The experimental results demonstrate that the ViT-L/14@336px encoder in CLIP achieves better performance in visual question answering tasks, with an accuracy of 61.49%. © 2024 ieee.

关键词： signal encoding

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Deep Joint Source-Channel Coding for Person Re-Identification With Related image Sources 23

Deep Joint Source-Channel Coding for Person Re-Identificatio...

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23rd ieee international conference on Communication technology, ICCT 2023

作者： Chu, Zhihang Chen, Wei Wang, Ning Ai, Bo State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Frontiers Science Center for Smart High-speed Railway System China Key Laboratory of Railway Industry of Broadband Mobile Information Communications China School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9798350325959

In this paper, we propose a deep learning based joint source-channel coding (JSCC) scheme for wireless transmission of multiple related image sources in person re-identification tasks. Our network utilizes parameter sharing for the encoder at the transmitter and adopts a joint decoding architecture for the decoder at the receiver, reducing storage space and computational resource consumption. By leveraging object-related information from multiple perspectives, the decoder fuses richer features to perform corresponding re-identification tasks. Experimental results demonstrate that the performance of our proposed method degrades gracefully as available bandwidth resources decrease, and the proposed multi-transmitter JSCC scheme achieves significant gains under low bandwidth and low signal-to-noise ratio conditions, compared with the existing approach. This work shows the potential of the JSCC-based approach for efficient and robust wireless transmission of related image sources in person re-identification scenarios. © 2023 ieee.

关键词： deep joint source and channel coding image retrieval person re-identification

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Real-Time Monitoring of Physiological signals Using Millimeter-Wave Radar and Convolutional Neural Network 4

Real-Time Monitoring of Physiological Signals Using Millimet...

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4th international conference on Electronic Information Engineering and Computer Communication, EIECC 2024

作者： Jia, Ye Liu, Yulei Tan, Wenjun School of Computer Science and Engineering Northeastern University Hunnan Liaoning Shenyang China Gd Midea Air-Conditioning Equipment Co. Ltd. Shunde Guangdong Foshan China

ISBN: (纸本)9798331534622

This paper presents a study on real-time monitoring technology for respiratory and heartbeat processes based on millimeter-wave radar. The core focus of this research is on achieving high real-time signal monitoring and recognition, capable of identifying the peak and smooth intervals of the respiratory process, as well as the moments of P, R, and T waves in the heartbeat process, all with minimal latency. This approach provides a more convenient and user-friendly solution for medical gating in CT, MRI, and other smart home applications. Through an in-depth investigation of millimeter-wave radar signal processing methods, an efficient algorithm for respiratory and heartbeat detection is proposed, which includes noise suppression, real-time respiratory-heartbeat separation, heartbeat signal reconstruction based on deep learning, signal peak finding, and signal gradual period identification. The algorithm is verified in real-world environments, and the fusion of traditional signal processing techniques with deep learning methods enables real-time monitoring and recognition of respiratory and heartbeat signals using cost-effective 24 GHz millimeter-wave radar. © 2024 ieee.

关键词： image segmentation

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16th international conference on Wireless Communications and signal Processing, WCSP 2024

16th International Conference on Wireless Communications and...

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16th international conference on Wireless Communications and signal Processing, WCSP 2024

ISBN: (纸本)9798350390643

The proceedings contain 253 papers. The topics discussed include: near-field attitude and location estimation based on partitioned large-scale antenna array;CKM-based environment-aware pilot reuse and channel estimation;analysis of sliding-correlation frame synchronization for visible light communication with signal-dependent noise;optimizing age of information for multi-antenna aided covert communications;PLMS: a packet loss memory-based transmission enhancement scheme for GEO satellite networks;interference management for IRS-NOMA networks via constructive interference;impacts of blockages on UAV-enabled integrated sensing and communications;robust image watermarking based on texture guidance and dynamic multi-spectrum attention;refined parity-check polar codes from PAC codes with lower complexity;and resource allocation in a dual-sensor update system for age of information minimization.

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OCTAMamba: A State-Space Model Approach for Precision OCTA Vasculature Segmentation

OCTAMamba: A State-Space Model Approach for Precision OCTA V...

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2025 ieee international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Zou, Shun Zhang, Zhuo Gao, Guangwei College of Artificial Intelligence Nanjing Agricultural University Nanjing China College of Computer Science and Technology National University of Defense Technology Changsha China Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China

ISBN: (纸本)9798350368741

Optical Coherence Tomography Angiography (OCTA) is a crucial imaging technique for visualizing retinal vasculature and diagnosing eye diseases such as diabetic retinopathy and glaucoma. However, precise segmentation of OCTA vasculature remains challenging due to the multi-scale vessel structures and noise from poor image quality and eye lesions. In this study, we proposed OCTAMamba, a novel U-shaped network based on the Mamba architecture, designed to segment vasculature in OCTA accurately. OCTAMamba integrates a Quad Stream Efficient Mining Embedding Module for local feature extraction, a Multi-Scale Dilated Asymmetric Convolution Module to capture multi-scale vasculature, and a Focused Feature Recalibration Module to filter noise and highlight target areas. Our method achieves efficient global modeling and local feature extraction while maintaining linear complexity, making it suitable for low-computation medical applications. Extensive experiments on the OCTA 3M, OCTA 6M, and ROSSA datasets demonstrated that OCTAMamba outperforms state-of-the-art methods, providing a new reference for efficient OCTA segmentation. Code is available at https://***/zs1314/OCTAMamba. © 2025 ieee.

关键词： Mamba OCTA image State Space Model

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The Design of Spectrally-Constrained Sequences with Periodic Zero-Correlation Zone

The Design of Spectrally-Constrained Sequences with Periodic...

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2024 international conference Optoelectronic Information and Optical Engineering, OIOE 2024

作者： Liu, Shicheng Peng, Xiuping Wang, Shide School of Information Science and Engineering Yanshan University Qinhuangdao066004 China Hebei Province Key Laboratory of Information Transmission and Signal Processing Qinhuangdao066004 China

ISBN: (数字)9781510688209

ISBN: (纸本)9781510688193

Spectrally-constrained sequences (SCSs) are primarily used in emerging systems operating over non-contiguous spectrum such as cognitive radio and cognitive radar. In order to obtain more SCSs, firstly, a novel type of SCS called periodic zero-correlation zone spectrally-constrained sequence(PZCZ-SCS) is proposed in this paper. Then, based on time-frequency synthesis analysis, the conditions that the PZCZ-SCSs must satisfy in terms of "spectral constraint" are derived. Specifically, it is a divisible difference set with parametersλ1 = k and λ1 > λ2. Finally, the construction of PZCZ-SCSs is presented by using the Chinese remainder theorem. Through this research, a new class of SCS is provided for cognitive radio. © 2025 SPIE.

关键词： image coding

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An Approach based on Multi-Entropy and Artificial Intelligence for Communication Modulation signal Identification

An Approach Based on Multi-Entropy and Artificial Intelligen...

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2023 ieee international conference on image Processing and Computer Applications, ICIPCA 2023

作者： Guo, Ziyuan Miao, Le Lin, Yifan School of Electronic and Information Engineering Beijing Jiaotong University Beijing China School of Communication and Information Engineering Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (纸本)9798350314670

With the increasingly complex electromagnetic influence in the modern communication environment and the explosive growth of communication business data, the modulation recognition system needs to constantly learn new modulation categories and signal characteristics in the process of data increment, and gradually acquire stronger modulation recognition capability. This paper proposes a communication modulation signal recognition method based on multi-entropy and artificial intelligence technology. Firstly, the power entropy, singular spectral entropy, sample entropy, fuzzy entropy, and permutation entropy of the signal are calculated, and a composite entropy feature calculation method based on entropy weight method is proposed to further study the feature extraction of the signal. Finally, it is found that when using neural network for learning, without feature extraction, the recognition accuracy rate is only 70.7%, while using the feature extraction method proposed in this paper, the accuracy rate can reach 89.5%, and the recognition speed is significantly improved. In addition, the feature extraction method proposed in this paper can improve all kinds of modulation recognition based on artificial intelligence. © 2023 ieee.

关键词： Feature extraction

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A Comparative Analysis of image Steganography Techniques in Spatial and Transform Domains

A Comparative Analysis of Image Steganography Techniques in ...

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2023 ieee international conference on Dependable, Autonomic and Secure Computing, 2023 international conference on Pervasive Intelligence and Computing, 2023 international conference on Cloud and Big Data Computing, 2023 international conference on Cyber Science and technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Shehada, Dina Bouridane, Ahmed University of Sharjah Department of Electrical Sharjah United Arab Emirates College of Engineering and It University of Dubai Dubai United Arab Emirates

ISBN: (纸本)9798350304602

image steganography is the study and practice of concealing information within images with the purpose of deceiving the viewer as if there is no information hidden within the images. Transferring the embedded information to the destination securely without being detected is considered the biggest challenge. In this paper, a comparative analysis of image steganography in both the spatial and a transform domains, particularly the Least Significant Bit (LSB) and Discrete Cosine Transform (DCT) was conducted. Two experiments were carried out to evaluate the performance of LSB and DCT based steganography techniques with respect to the quality of the generated stego images and their robustness against some known attacks. ALASKA2 and BOSSBase datasets were utilized in this study. The results show that the DCT based stenography technique outperforms the LSB counterpart as it generates stego images with higher quality and higher (Peak to signal Ratio) PSNR values. It was also shown that DCT steganography is more robust against steganalysis. © 2023 ieee.

关键词： Discrete cosine transforms

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Intelligent Automation System for Energy Conservation Using IoT and Machine Learning 7

Intelligent Automation System for Energy Conservation Using ...

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7th international conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： Kumar, B.Sakthi Ramalingam, S. Viveka, S. Vigneshwar, V.G. Sivasubramaniam, N. Maria, J.Sancta Sri Eshwar College of Engineering Department of ECE Coimbatore India

ISBN: (纸本)9798350340600

People's busy and comfortable lifestyles in our technological era have enabled communication technology to advance to the point where any information may be accessed from anywhere in the world. Technological revolutions such as the internet of Things and machine learning have grown in popularity in recent years due to the benefits they represent in a variety of domains of expertise. It is worth noting that implementing these two technologies encourages the development of more and better-automated systems of control in the home automation area that adapt to the preferences of each user. Many instances occur at establishments as a result of negligence and forgetfulness, when electrical equipment is left on even when there is no human presence in the room. This is one of the most prominent examples of societal electrical waste. As a result, a smart system that ensures both efficacy and efficiency must be implemented. To solve this issue, a privacy-preserving and safe identification and verification methodology for Home Automation systems that leverages strong current technologies such as IoT, Machine Learning (ML), and image Processing are proposed. It additionally makes utilization of more efficient image processing technologies than IR sensor-based systems for home automation. The ML Algorithm is responsible for recognizing people in each frame captured by the camera. A robust machine learning system capable of accurately distinguishing diverse environmental variables and human activities is designed to manage challenging situations. © 2023 ieee.

关键词： internet of things

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