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2024 International conference on Information Technology, Communication Ecosystem and Management, ITCEM 2024

作者： Luo, Shenghan Wang, Xinyan Southeast University Nanjing211189 China Xuzhou Pharmaceutical Vocational College Xuzhou221116 China

ISBN: (纸本)9798331542696

The quality of image signals directly affects the performance of intelligent communication systems. This paper proposes a set of image enhancement and denoising algorithms to address image quality degradation in intelligent communication systems. For image enhancement, we designed an adaptive histogram equalization algorithm based on blocks and a contrast adaptive optimization method, and implemented a detail enhancement algorithm combining multi-scale edge detection and enhancement. In terms of image denoising, we proposed adaptive median filtering, improved soft-threshold wavelet domain denoising, and non-local means algorithms to effectively suppress various types of noise. The system is developed using a hybrid \mathrm{C}++ and Python framework with parallel processing achieved through multithreading technology. Experimental results show that the proposed algorithms significantly improve image quality, processing efficiency, and system stability, providing reliable image signal processing support for intelligent communication systems. © 2024 IEEE.

关键词： C++ (programming language)

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Ternary Systolic Array Architecture for Matrix Multiplication in CNFET-Memristor Technology 19

Ternary Systolic Array Architecture for Matrix Multiplicatio...

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Asia Pacific conference on Circuits and systems (APCCAS)

作者： Thakur, Shivani Bodapati, Srinivasu Indian Inst Technol Mandi Sch Comp & Elect Engn Mandi Himachal Prades India

ISBN: (纸本)9798350381191

This paper presents ternary systolic array architecture for matrix multiplication for ternary neural networks and image processing algorithms in ternary logic. As part of the architecture, we have proposed a processing element of a systolic array architecture. The processing element is designed using a partial product calculator, which does the multiply-accumulate operation using single-digit multipliers and adders. All proposed circuits are designed in CNFET-Memristor technology and simulated using Cadence Virtuoso. The propagation delay, power, and power delay product of the proposed circuits are calculated using simulations. The proposed matrix multiplication is used in the Gaussian smoothing of images and results were presented.

关键词： Systolic array ternary matrix multiplication image processing processing element

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Real Time Performance Evaluation of Deep Learning algorithms in image Recognition under the Pytorch Framework

Real Time Performance Evaluation of Deep Learning Algorithms...

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2024 International conference on Intelligent algorithms for Computational Intelligence systems, IACIS 2024

作者： Lu, Di Liu, Shengzhi Xinjiang College of Science & Technology Korla China

ISBN: (纸本)9798350360660

With the rapid development of artificial intelligence technology, deep learning has become one of the key technologies in the field of image recognition. PyTorch has become the preferred framework for researchers due to its flexibility, efficiency, and ease of use. This study focuses on the real-time performance of deep learning algorithms in image recognition under the PyTorch framework, and its effectiveness is verified through system experiments. The experiment revealed that the algorithm under this framework exhibits excellent real-time performance in image recognition tasks, with an average frame rate of up to 59.46 FPS and an image processing delay as low as 59.44 milliseconds, fully meeting the demand for efficient processing in a wide range of application scenarios. This discovery demonstrates the powerful potential and practicality of PyTorch in the field of image recognition. © 2024 IEEE.

关键词： Adversarial machine learning

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HQ-I2IT: Redesign the optimization scheme to improve image quality in CycleGAN-based image translation systems

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IET image processing 2024年第2期18卷 507-522页

作者： Zhang, Yipeng Hu, Bingliang Huang, Yingying Gao, Chi Yin, Jianfu Wang, Quang Chinese Acad Sci Xian Inst Opt & Precis Mech Key Lab Spectral Imaging Technol 17 Informat Ave Xian 710119 Shaanxi Peoples R China Key Lab Biomed Spect Xian Xian Shaanxi Peoples R China Univ Chinese Acad Sci Sch Optoelect Beijing Peoples R China

The image-to-image translation (I2IT) task aims to transform images from the source domain into the specified target domain. State-of-the-art CycleGAN-based translation algorithms typically use cycle consistency loss and latent regression loss to constrain translation. In this work, it is demonstrated that the model parameters constrained by the cycle consistency loss and the latent regression loss are equivalent to optimizing the medians of the data distribution and the generative distribution. In addition, there is a style bias in the translation. This bias interacts between the generator and the style encoder and visually exhibits translation errors, e.g. the style of the generated image is not equal to the style of the reference image. To address these issues, a new I2IT model termed high-quality-I2IT (HQ-I2IT) is proposed. The optimization scheme is redesigned to prevent the model from optimizing the median of the data distribution. In addition, by separating the optimization of the generator and the latent code estimator, the redesigned model avoids error interactions and gradually corrects errors during training, thereby avoiding learning the median of the generated distribution. The experimental results demonstrate that the visual quality of the images produced by HQ-I2IT is significantly improved without changing the generator structure, especially when guided by the reference images. Specifically, the Frechet inception distance on the AFHQ and CelebA-HQ datasets are reduced from 19.8 to 10.2 and from 23.8 to 17.0, respectively. In this work, it is demonstrated that the cycle consistency loss and latent regression loss in CycleGAN-based image translation models can be detrimental to image quality. The optimization scheme of CycleGAN-based image translation systems is redesigned and a new translation model named HQ-I2IT is proposed. Experiments demonstrate that the proposed method can significantly improve image quality and translation ***

关键词： computer animation computer vision image processing

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Liquid Filling Quality Control System for Beverage Industry Using image processing (CNN) 24

Liquid Filling Quality Control System for Beverage Industry ...

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24th International conference on Control, Automation and systems

作者： Chatchapol, B. Autanan, W. Anan, W. Rajamangala Univ Technol Lanna Fac Elect Engn Dept Elect Engn Chiang Mai 50300 Thailand Rajamangala Univ Technol Lanna Fac Engn Dept Elect Engn & Automat Control Syst Chiang Mai Thailand Chiang Mai Univ Fac Engn Robot Engn & AI Chiang Mai 50200 Thailand

ISBN: (纸本)9798331517939;9788993215380

The beverage industry has been leveraging automation to enhance production efficiency. An essential advancement involves the use of AI and image processing to ensure the high quality of products. image processing systems employ sophisticated algorithms to meticulously inspect specific aspects of the production process (liquid level, lid sealing, text print). The CNN algorithm is primarily utilized to scrutinize water bottle images to verify their quality. Maintaining consistent production speed poses a challenge, underscoring the importance of examining the image processing system from various perspectives. The research proposed implementing a system that oversees liquid filling quality through an image processing algorithm, a camera module, and conveyor belt speed control to address. This system monitors liquid levels and bottle capping while the production line is operational. On average, the system achieves a 98.89 percent accuracy rate in detecting liquid levels and a 100 percent accuracy rate in sealing the lids. Furthermore, the conveyor speed control can adjust automatically to detect variations in product movement speed.

关键词： Beverage Liquid Filling image processing CNN algorithm

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REMOVING REFLECTIVE FLARE IN REAL-WORLD CONDITIONS 31

REMOVING REFLECTIVE FLARE IN REAL-WORLD CONDITIONS

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2024 International conference on image processing

作者： Lan, Fengbo Chen, Chang Wen Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

ISBN: (纸本)9798350349405;9798350349399

The increasing prevalence of mobile devices has led to significant advancements in mobile camera systems and improved image quality. Nonetheless, mobile photography still grapples with flare corruptions such as reflective flare. The absence of a comprehensive real image dataset tailored for mobile phones hinders the development of effective flare mitigation techniques. To address this issue, we present a novel real image dataset specifically designed for mobile camera systems, focusing on flare removal. Capitalizing on the distinct properties of real images, this dataset serves as a solid foundation for developing advanced flare removal algorithms. The dataset comprises over 1,100 pairs of high-quality, full-resolution images for reflective flare, which generate 2,200 paired patches, ensuring broad adaptability across various imaging conditions. Experimental results demonstrate that networks trained with synthesized data struggle to cope with the complex lighting settings present in this real image dataset. Our dataset is expected to enable an array of new research in flare removal and contribute to substantial improvements in mobile image quality, benefiting mobile photographers and end-users alike.

关键词： flare removal reflective flare real image dataset

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Application of Various Novel Computer image processing (CIP) algorithms in Digital Media Art images

Application of Various Novel Computer Image Processing (CIP)...

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2025 International conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Du, Xian Shanghai Institute of Visual Arts Shanghai201620 China

ISBN: (纸本)9798331509828

With the continuous development of digital image processing algorithms, its application scenarios have been integrated from the simple research of a single image and a single algorithm to a multi-algorithm fusion analysis paradigm. Therefore, this study proposes a digital media art image analysis framework that combines multiple computer image processing (CIP) algorithms. This breakthrough covers three core architectures: image edge extraction, image style transfer, and image compression. In the image edge extraction module, this study designed an improved edge detection algorithm based on de-noising auto-encoder. This algorithm improves the accuracy of edge detection through multi-directional feature extraction and (I, O)-fuzzy rough set optimization, while maintaining global stability. In the image style transfer module, this study proposed a dual-module network. The network includes a texture translation network and a perceptual loss network, and achieves cross-domain style transfer through a fusion model. At the same time, the algorithm optimizes the perceptual loss function to enhance semantic matching capabilities. In the image compression module, this study uses chaotic systems to construct an optimized measurement matrix. Specifically, through a distributed data parallel training framework, a compression method based on the LPAC gradient sparse algorithm is constructed. By testing the three modules separately, the experimental results confirm the effectiveness of the proposed algorithm. © 2025 IEEE.

关键词： image compression

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Deep learning for plant stress detection: A comprehensive review of technologies, challenges, and future directions

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2025年 229卷

作者： Paul, Nijhum Sunil, G. C. Horvath, David Sun, Xin North Dakota State Univ Dept Agr & Biosyst Engn Fargo ND 58108 USA North Dakota State Univ Genom Phen & Bioinformat Program Fargo ND 58108 USA USDA ARS ETSARC Weed & insect Biol unit Fargo ND 58102 USA

Deep learning (DL)-based systems have emerged as powerful methods for the diagnosis and treatment of plant stress, offering high accuracy and efficiency in analyzing imagery data. This review paper aims to present a thorough overview of the state-of-the-art DL technologies for plant stress detection. For this purpose, a systematic literature review was conducted to identify relevant articles for highlighting the technologies and approaches currently employed in the development of a DL-based plant stress detection system, specifically the advancement of image-based data collection systems, image preprocessing techniques, and deep learning algorithms and their applications in plant stress classification, disease detection, and segmentation tasks. Additionally, this review emphasizes the challenges and future directions in collecting and preprocessing image data, model development, and deployment in real-world agricultural settings. Some of the key findings from this review paper are: Training data: (i) Most plant stress detection models have been trained on Red Green Blue (RGB) images;(ii) Data augmentation can increase both the quantity and variation of training data;(iii) Handling multimodal inputs (e. g., image, temperature, humidity) allows the model to leverage information from diverse sources, which can improve prediction accuracy;Model Design and Efficiency: (i) Self-supervised learning (SSL) and Few-shot learning (FSL)-based methods may be better than transfer learning (TL)-based models for classifying plant stress when the number of labeled training images are scarce;(ii) Custom designed DL architectures for a specific stress and plant type can have better performance than the state-of-the-art DL architectures in terms of efficiency, overfitting, and accuracy;(iii) The multi-task learning DL structure reuses most of the network architecture while performing multiple tasks (e.g., estimate stress type and severity) simultaneously, which makes the learning much

关键词： Plant stress Computer vision Deep learning image processing Imaging sensor Precision agriculture

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Impacts of image Compression on the Detection Quality of a Novel Real-Time image processing Platform 26

Impacts of Image Compression on the Detection Quality of a N...

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IEEE 26th International conference on Intelligent Transportation systems (ITSC)

作者： Mehrke, Jannik Volk, Georg Stumpp, Yannik Bringmann, Oliver Terzis, Anestis Ulm Univ Appl Sci Inst Commun Technol Ulm Germany Univ Tubingen Dept Comp Engn Tubingen Germany

ISBN: (纸本)9798350399462

Cloud-based data processing latency mainly depends on the transmission delay of data to the cloud and the used data processing algorithm. To minimize the transmission delay, it is important to compress the transferred data without reducing the quality of the data. When using data compression algorithms, it is important to validate the impact of these algorithms on the detection quality. This work evaluates the effects of image compression and transmission over wireless interfaces on state of the art neural networks. Therefore, a modern image processing platform for next generation automotive processing architectures, as used in software defined vehicles, is introduced. The impacts of different image encoders as well as data transmission parameters are investigated and discussed.

关键词： image compression

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A Compact Real-Time Thermal Imaging System Based on Heterogeneous System-on-Chip 30

A Compact Real-Time Thermal Imaging System Based on Heteroge...

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30th IEEE International conference on Embedded and Real-Time Computing systems and Applications (RTCSA)

作者： Oh, Hyun Woo Choi, Cheol-Ho Cha, Jeong Woo Choi, Hyunmin Shin, Jung-Ho Han, Joon Hwan Hanwha Syst Infra Technol R&D Ctr Seongnam South Korea

ISBN: (纸本)9798350387964;9798350387957

This paper presents a real-time embedded thermal imaging system architecture for compact, energy-efficient, high-quality imaging utilizing heterogeneous system-on-chip (SoC) and uncooled infrared focal plane arrays (IRFPAs). Unlike previous systems that organized separate devices for complex image processing, our system provides integrated image processing support for robust sensor-to-surveillance. The image processing organizes two algorithm stacks: a non-uniformity correction stack to mitigate the distinctive noise vulnerabilities of uncooled IRFPAs, and an image enhancement stack including contrast enhancement and temporal noise filters. We optimized these algorithms for domain-specific factors, including asymmetric multiprocessing (AMP), cache organization, single instruction multiple data (SIMD) instructions, and very long instruction word (VLIW) architectures. The implementation on the TI TDA3x SoC demonstrates that our system can process 640x480, 60 frames per second (FPS) videos at a peak core load of 57.5% while consuming power less than 2.2 W for the entire system, denoting the possibility of processing the 1280x1024, 30 FPS videos from the cutting-edge uncooled IRFPAs. Additionally, our system improves power efficiency by 9.42% and 9.96% at 30 and 60 FPS, respectively, compared to the state-of-the-art when executing similar image processing algorithms.

关键词： Thermal Imaging image processing Heterogeneous Computing SIMD Real-Time Embedded systems

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