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检索条件"任意字段=IEEE Visual Communications and Image Processing Conference"

共 25554 条记录，以下是411-420 订阅

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Enhancement of Underwater images using Multi-Scale Retinex, CLAHE and Unsharp Masking Techniques 2

Enhancement of Underwater Images using Multi-Scale Retinex, ...

引用

2nd ieee International conference on Networking and communications, ICNWC 2024

作者： Gamini, Sridevi Reddy Pothamsetti, Bhavya Prasanna Malla, Baby Gayathri Podila, Hanuman Vuda, Manjusree Prasad, Satti Harichandra Aditya Engineering College Dept. of ECE Surampalem India

ISBN: (纸本)9798350365269

Underwater environments present unique challenges for image capture and analysis due to factors like light attenuation, color distortion, and loss of detail. In this study, we propose a comprehensive strategy to underwater image enhancement employing Color Compensation, Detail Sharpening, Auto-level based Multi-Scale Retinex, Contrast Limited Adaptive Histogram Equalization (CLAHE). Additionally, we integrate Unsharp masking technique to further improve image quality and enhance visual clarity. Through experimental validation using real-world underwater imagery datasets, this method demonstrates significant advancements in enhancing underwater scenes, effectively mitigating color distortions and restoring lost details. © 2024 ieee.

关键词： image enhancement

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Blind image Quality Assessment Score for Humanities Online Digital Repositories 9

Blind Image Quality Assessment Score for Humanities Online D...

引用

9th International conference on Smart and Sustainable Technologies (SpliTech)

作者： Tomasovic, Zeljka Pintaric, Neven Saulig, Nicoletta Univ Zadar Dept Informat Sci Zadar Croatia Univ Pula Fac Engn Pula Croatia

ISBN: (纸本)9798350390797;9789532901351

The proliferation of digital image repositories has revolutionized the accessibility of visual data across various domains, from scientific research to cultural heritage preservation. However, the quality of images within these repositories varies significantly, posing a challenge for users relying on accurate and high-quality visual information. In recent years, a substantial effort has been made to find an objective and blind image quality assessment (IQA) metric that can correlate with perceived quality measurements. Unfortunately, there is still no definite method to asses this problem because of the complex nature of objectively measuring image distortions. In this paper, we present a comprehensive framework for assessing image quality in online repositories for humanities. The approach relies on a combination of known image processing techniques, including histogram analysis, artefact estimation and dynamic range evaluation. Other parameters are also taken into account, such as resolution, dots per inch (DPI), bit depth and format. By automatically grading image quality using these methods, we aim to provide researchers and practitioners within the humanities field, with a reliable tool for evaluating the trustworthiness and suitability of images sourced from national repositories. Through a systematic examination of image quality metrics, the strengths and weaknesses of existing image collections can be highlighted, paving the way for enhanced curation standards and improved access to high-quality visual resources. Methods were tested on images from online repositories in public domain.

关键词： digital object digital image image quality assessment noise artefacts histogram repository

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TRUNCATED WEIGHTED NUCLEAR NORM REGULARIZATION AND SPARSITY FOR image DENOISING 30

TRUNCATED WEIGHTED NUCLEAR NORM REGULARIZATION AND SPARSITY ...

引用

30th ieee International conference on image processing (ICIP)

作者： Zhang, Ming Yan Zhang, Mingli Zhao, Feng Zhang, Fan Liu, Yepeng Evans, Alan Shandong Technol & Business Univ Yantai 264025 Peoples R China McGill Univ Montreal Neurol Inst Mcgill Ctr Integrat Neuroscience Montreal PQ Canada

ISBN: (纸本)9781728198354

The attribute of signal sparsity is widely used to sparse representaion. The existing nuclear norm minimization and weighted nuclear norm minimization may achieve a suboptimal in real application with the inaccurate approximation of rank function. This paper presents a novel denoising method that preserves fine structures in the image by imposing L-1 norm constraints on the wavelet transform coefficients and low rank on high-frequency components of group similar patches. An efficient proximal operator of Truncated Weighted Nuclear Norm (TWNN) is proposed to accurately recover the underlying high-frequency components of low rank patches. By combining a wavelet domain sparse preservation prior with TWNN, the proposed method significantly improves the reconstruction accuracy, leading to a higher PSNR/SSIM and visual quality than state of the art approaches.

关键词： image denoising wavelet domain sparsity low rank TWNN

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LED Strip Quality Detection Based on OpenCV 22

LED Strip Quality Detection Based on OpenCV

引用

22nd International conference on Optical communications and Networks, ICOCN 2024

作者： Liu, Hao Huang, Qihao Liu, Honglin Lang, Tingting College of Optoelectronic Technology China Jiliang University Hangzhou China Hangzhou Leaper Technology Co. Ltd Hangzhou China

An image processing algorithm for real-time examination of LED light strips is proposed, which enables quick detection of blind LED beads in strips. It is successfully used in production line to replace manual inspect... 详细信息

ISBN: (纸本)9798350367652

关键词： image processing

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AG-Mono: A Monocular Dataset for Unmanned Air Vehicles 30

AG-Mono: A Monocular Dataset for Unmanned Air Vehicles

引用

30th ieee Signal processing and communications Applications conference (SIU)

作者： Simsek, Bugra Giden, Ibrahim Halil Bilge, Hasan Sakir ASELSAN Girisimcilik Merkezi Ankara Turkey Gazi Univ Ankara Turkey

ISBN: (数字)9781665450928

ISBN: (纸本)9781665450928

One of the most important information needed while performing unmanned aerial vehicles (UAV) operations is about the platform location and the environment. Such platforms mostly use GNSS signals outdoors. However, in indoor areas where GNSS signals cannot be received or in situations where signals are jammed, it is not possible to obtain location information using these signals. For that reason, alternative navigation systems have become so crucial. One of the most preferred systems among navigation technologies is the visual simultaneous localization and mapping (vSLAM) method performed using RGB cameras on the UAVs. In this study, an open monocular image dataset called AG-Mono was created and published online to test the performance of vSLAM algorithms. This dataset was created at three different exposure times using a handheld platform, and it includes video sequences at 640x480 image resolution. The experimental area where the images were created is a closed corridor with 16.5 x 4.5 meters and four sharp corners.

关键词： Unmanned Aerial Vehicles (UAVs) visual SLAM (vSLAM) Monocular image Dataset

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PTS-LIC: Pruning Threshold Searching for Lightweight Learned image Compression

PTS-LIC: Pruning Threshold Searching for Lightweight Learned...

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2023 ieee International conference on visual communications and image processing, VCIP 2023

作者： Luo, Ao Sun, Heming Liu, Jinming Lin, Fangzheng Katto, Jiro Yokohama National University Faculty of Engineering Kanagawa Japan Shanghai Jiao Tong University China Waseda University Department of Computer Science and Communication Engineering Tokyo Japan Waseda Research Institute for Science and Engineering Tokyo Japan

ISBN: (纸本)9798350359855

Learned image Compression (LIC), which uses neural networks to compress images, has experienced significant growth in recent years. The hyperprior-module-based LIC model has achieved higher performance than classical codecs. However, the LIC models are too heavy (in calculation and parameter amounts) to apply to edge devices. To solve this problem, some former papers focus on structural pruning for LIC models. However, they either cause noticeable performance decrement or neglect the appropriate pruning threshold for each LIC model. These problems keep their pruning results sub-optimal. This paper proposes a Pruning Threshold Searching on the hyperprior module for different-quality LIC models. Our method removes most parameters and calculations while keeping the performance the same as the models before pruning. We removed at least 49.8% of parameters and 28.5% of calculations for the Channel-Wise-Context-Model-based models and 29.1% of parameters for the Cheng-2020 models. © 2023 ieee.

关键词： image compression

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Filtering Methods, Assessment Criteria, and Prospects for image Denoising

Filtering Methods, Assessment Criteria, and Prospects for Im...

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2025 ieee International Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Wandile, Piyush S Singh Kirar, Bhupendra Kirar, Seema Indian Institute of Information Technology Computer Science and Engineering Bhopal India Indian Institute of Information Technology Electronics and Communications Engineering Bhopal India Bansal Institute of Science and Technology Electronics and Communications Engineering Bhopal India

ISBN: (纸本)9798331529833

image denoising is a crucial step in image acquisition and processing that helps improve the image quality by removing the unwanted noise. In this paper Gaussian and median filters are used as denoiser and performance is evaluated using some of the key metrics like Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Feature Similarity Index Measure (FSIM), Structural Similarity Index Measure (SSIM), Mean Absolute Error (MAE), and Correlation Coefficient. A denoiser with low MSE, high PSNR, values of 1 for SSIM, FSIM, and Correlation Coefficient. Reported results are better in comparison to existing for better image quality. © 2025 ieee.

关键词： Wiener filtering

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A multi-modal feature fusion-based approach for Chest X-ray Report Generation 11

A multi-modal feature fusion-based approach for Chest X-ray ...

引用

11th International conference on Wireless Networks and Mobile communications (WINCOM) - Inclusive and Intelligent Connectivity

作者： Cheddi, Fatima Habbani, Ahmed Nait-Charif, Hammadi Mohammed V Univ ENSIAS Smart Syst Lab Rabat Morocco ParisSaclay Univ Paris France Evry Univ Evry France Univ Bordeaux CNRS LaBRI Bordeaux INP Talence France Bournemouth Univ Natl Ctr Comp Animat Bournemouth Dorset England

ISBN: (纸本)9798350377873;9798350377866

Given the growing dependence on medical imaging, there is a significant requirement for automated report generation, which can save the radiologist's time and reduce the possibility of diagnostic errors. Existing approaches face various difficulties, including insufficient professionalism, a variety of diseases, and fluency in reports. These problems are the result of the use of an encoder-decoder deep learning architecture to establish a uni-directional image-to-report relationship and neglect the bidirectional connections between images and reports, making it challenging to establish the intrinsic medical correlations between them. To this end, we propose a novel approach for chest radiology report generation based on multimodal feature fusion. Our method uses textual and visual features that are taken from medical chest X-ray images and their real reports. Firstly, we use a vision transformer to extract visual features from medical images;on the other hand, we use the Word2Vec model to extract semantic features from textual medical reports. Additionally, we employ advanced techniques such as channel attention networks and cross- modal information fusion modules to enhance the quality and coherence of the generated reports. We have evaluated our proposed approach on two publicly available chest X-ray datasets, IU X-ray and NIH. The results show that our approach outperforms state-of-the-art methods. Particularly in the ROUGE metric and BLEU metric.

关键词： Chest X-ray report generation Multi-modal learning medical image Deep learning Transformers Medical report

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DVI-SLAM: A Dual visual Inertial SLAM Network

DVI-SLAM: A Dual Visual Inertial SLAM Network

引用

ieee International conference on Robotics and Automation (ICRA)

作者： Peng, Xiongfeng Liu, Zhihua Li, Weiming Tan, Ping Cho, SoonYong Wang, Qiang Samsung R&D Inst China SAIT China Lab Beijing Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Samsung Adv Inst Technol Multimedia Syst TU Seoul South Korea

ISBN: (纸本)9798350384581;9798350384574

Recent deep learning based visual simultaneous localization and mapping (SLAM) methods have made significant progress. However, how to make full use of visual information as well as better integrate with inertial measurement unit (IMU) in visual SLAM has potential research value. This paper proposes a novel deep SLAM network with dual visual factors. The basic idea is to integrate both photometric factor and re-projection factor into the end-to-end differentiable structure through multi-factor data association module. We show that the proposed network dynamically learns and adjusts the confidence maps of both visual factors and it can be further extended to include the IMU factors as well. Extensive experiments validate that our proposed method significantly outperforms the state-of-the-art methods on several public datasets, including TartanAir, EuRoC and ETH3D-SLAM. Specifically, when dynamically fusing the three factors together, the absolute trajectory error for both monocular and stereo configurations on EuRoC dataset has reduced by 45.3% and 36.2% respectively.

关键词： Stereo image processing

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EFFICIENT SCENE TEXT image SUPER-RESOLUTION WITH SEMANTIC GUIDANCE 49

EFFICIENT SCENE TEXT IMAGE SUPER-RESOLUTION WITH SEMANTIC GU...

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49th ieee International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： TomyEnrique, Leowu Du, Xiangcheng Liu, Kangliang Yuan, Han Zhou, Zhao Jin, Cheng Fudan Univ Sch Comp Sci Shanghai Peoples R China Videt Technol Shanghai Peoples R China MCT Innovat Ctr Callig & Painting Creat Technol Beijing Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Scene text image super-resolution has significantly improved the accuracy of scene text recognition. However, many existing methods emphasize performance over efficiency and ignore the practical need for lightweight solutions in deployment scenarios. Faced with the issues, our work proposes an efficient framework called SGENet to facilitate deployment on resource-limited platforms. SGENet contains two branches: super-resolution branch and semantic guidance branch. We apply a lightweight pre-trained recognizer as a semantic extractor to enhance the understanding of text information. Meanwhile, we design the visual-semantic alignment module to achieve bidirectional alignment between image features and semantics, resulting in the generation of high-quality prior guidance. We conduct extensive experiments on benchmark dataset, and the proposed SGENet achieves excellent performance with fewer computational costs.

关键词： Scene text image super-resolution efficient model semantic guidance

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