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检索条件"任意字段=17th Image Processing: Algorithms and Systems Conference, IPAS 2019"

共 249 条记录，以下是1-10 订阅

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Quantum Variational Singular Value Decomposition: A new hybrid approach towards quantum image compression 17

Quantum Variational Singular Value Decomposition: A new hybr...

引用

17th International conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Britant Tiwari, Abhishek CDAC Uttar Pradesh Noida India

ISBN: (纸本)9798331531195

this research paper explores the application of singular value decomposition (SVD) in quantum image processing (QIP), specifically focusing on the computation of eigenvalues using variational quantum algorithms. SVD is a powerful mathematical tool in image processing, used for tasks such as image compression, noise reduction, and feature extraction. In this study, we propose quantum variational singular value decomposition (QVSVD) based on the variational quantum deflation (VQD) algorithm to determine the eigenvalues that contributes in calculating the singular values of the image matrix and eventually doing the SVD. this facilitates the extraction of eigenvalues with exponential speedup on real hardware compared to classical methods. We detail the implementation of this quantum algorithm within the framework of QIP, highlighting its advantages in terms of computational efficiency and accuracy on a Fault-Tolerant Quantum Computing (FTQC). Furthermore, we present a comparative analysis of the quantum and the classical method of SVD, demonstrating the accuracy of the image data. the simulation results validate the theoretical advantages of using quantum algorithms for SVD in image processing. this work not only underscores the potential of quantum computing in enhancing image processing techniques but also sets the stage for future research in the field, exploring more complex image processing tasks and other quantum algorithms. Our findings suggest that quantum image processing can offer unprecedented capabilities, paving the way for advancements in various applications such as medical imaging, remote sensing, and multimedia processing. © 2025 IEEE.

关键词： Noise abatement

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All-In-One image Restoration in Adverse Weather Conditions with Spatial-Frequency Domain Information 17th

All-In-One Image Restoration in Adverse Weather Conditions w...

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17th International conference on Intelligent Robotics and Applications

作者： Jing, Panfeng Liang, Yudong Wang, Siyu Gao, Pin An, Hongqiang Shanxi Univ Sch Comp & Informat Technol Taiyuan Shanxi Peoples R China Shanxi Univ Key Lab Computat Intelligence & Chinese Informat Minist Educ Taiyuan Shanxi Peoples R China Qingdao Htinvent Intelligent Technol Co Ltd Qingdao Shandong Peoples R China

ISBN: (纸本)9789819607730;9789819607747

Recently, the collaborative operations of multi-domain unmanned vehicle systems have garnered significant attention, particularly regarding their visual perception capabilities under various complex weather conditions. Adverse weather such as rain, snow, and fog can severely degrade image quality, thereby affecting the efficiency and safety of unmanned vehicle systems to a certain extent. therefore, image enhancement algorithms capable of handling multiple complex weather conditions are crucial for improving the visual perception capabilities of unmanned vehicle systems. this paper presents a unified image enhancement algorithm designed to address various complex weather conditions, such as rain, snow, and fog. the algorithm restores images by integrating spatial and frequency domain information. Experimental results fully demonstrate that this method can significantly enhance images under different adverse weather conditions and greatly improve image quality.

关键词： Deep Learning image Enhancement Adverse Weather Conditions

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17th Asian conference on Intelligent Information and Database systems, ACIIDS 2025

17th Asian Conference on Intelligent Information and Databas...

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17th Asian conference on Intelligent Information and Database systems, ACIIDS 2025

ISBN: (纸本)9789819660049

the proceedings contain 29 papers. the special focus in this conference is on Intelligent Information and Database systems. the topics include: GAMER-Pong: Game Adjustment by Monitoring Emotional Response;Machine Learning algorithms Comparison for Hand sEMG-Recorded Movements Classification;boosting Face Super-Resolution and Identification with Knowledge Distillation and Frequency Domain Loss;improving Efficiency image Captioning by Using Attention Mechanism Combined with Knowledge Graph;integrating Topological Data Analysis and Deep Learning: A Case Study in Cardiovascular Disease Prediction at thu Duc Hospital;boosting Lightweight Multi-branch Network by Integrating Segment, Skeleton, and Attribute Information;continuous Recognition of Mouth Patterns in Japanese Sign Language for Visual Communication;Towards an UAV Visual Navigation System Based on Map processing and Spatial Matching Techniques: A Literature Review;image Enhancement with Boosted Schrödinger Bridge;kernel-Level Energy-Efficient Neural Architecture Search for Tabular Dataset;estimation of Distribution algorithms with Overlapped Subpopulations;energy-Efficient Edge Query processing for Smart City Using Query Prediction;influence Maximization with Fairness Cost on Groups in Online Social Networks;TMMP: Efficient Continual Learning via Mixture of LoRA Experts and Top Maximum Magnitude Parameter Fusion;ADMEdge: Accelerating Edge Computing through Co-design for Enhanced Data Mobility;the Recursive Scheme of Clustering;TOPSIS-Inspired Socio-cognitive Mutation Operator for Metaheuristics;feature-Based Drift Detection in Non-stationary Data Streams Using Multiple Classifiers: A Comprehensive Analysis;a Feature Transformation Technique for Improving Ensemble Learning systems;explainable Artificial Intelligence to Improve Interpretability in Predictive Mutation Testing;an Analysis of Class Imbalance Challenges in Predictive Mutation Testing;efficient Vietnamese Name Detection Using Highly Discriminative N-Gra

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17th Asian conference on Intelligent Information and Database systems, ACIIDS 2025

17th Asian Conference on Intelligent Information and Databas...

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17th Asian conference on Intelligent Information and Database systems, ACIIDS 2025

ISBN: (纸本)9789819660070

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Quantum Variational Singular Value Decomposition: A new hybrid approach towards quantum image compression

Quantum Variational Singular Value Decomposition: A new hybr...

引用

International Communication systems and Networks and Workshops, COMSNETS

作者： Britant Abhishek Tiwari CDAC Noida Uttar Pradesh India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

关键词： Training Quantum computing Quantum algorithm image coding Accuracy Feature extraction Eigenvalues and eigenfunctions Matrix decomposition Time complexity Singular value decomposition

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Quantum-Enhanced Machine Learning for Precision Breast Cancer Diagnosis

Quantum-Enhanced Machine Learning for Precision Breast Cance...

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International Communication systems and Networks and Workshops, COMSNETS

作者： Agniva Banerjee Dept. Electrical Engineering and Computer Science Indian Institute of Science Education and Research Bhopal Bhopal India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Cancer is the leading cause of premature mortality. Globally, the prevalence of cancer is on the rise, and early-stage diagnosis is crucial for cancer recovery and survival. However, the presence of circulating tumor cells (CTCs) and heterogeneity within the tumor makes it hard to track how cancer changes over time and how resistance to treatment develops. Unfortunately, the cancer diagnosis precision remains inadequate, endangering precious human lives and necessitating costly procedures for accurate treatment. Machine learning (ML) has proven capable of discovering meaningful information and making predictions, particularly with large datasets. Moreover, quantum computers (QCs) inherently possess superposition and parallel computation, features not found in classical computing. Researchers have demonstrated that quantum neural networks (QNNs) achieve a significantly better effective dimension than classical NNs. In this context, this study investigates the application of classical and quantum machine learning (QML) algorithms to classify breast cancer images. Region-based segmentation is used to identify regions of interest and image processing algorithms are performed on segmented breast cancer images to extract meaningful features (e.g., perimeter, area, compactness). An user interface (UI) based software is created to swiftly distinguish benign and malignant instances during image analysis. Although QNNs perform poorly than the classical NNs, there is still a long way to go in developing feature maps and techniques for better performance. In contrast, the classical NNs have gone a long way with well-established algorithms and packages available to us. this research shows that with further development, QML has the potential to enhance cancer diagnosis accuracy and speed, ultimately improving early detection and treatment outcomes.

关键词： image segmentation Accuracy Machine learning algorithms Software algorithms Artificial neural networks Machine learning Feature extraction Breast cancer Software Tumors

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Evaluating the effectiveness of image quality metrics in a light field scenario 17

Evaluating the effectiveness of image quality metrics in a l...

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17th image processing: algorithms and systems conference, ipas 2019

作者： Arru, Giuliano Battisti, Federica Carli, Marco Department of Engineering Universitá degli Studi Roma Tre Rome Italy

In this contribution, an objective metric for quality evaluation of light field images is presented. the method is based on the exploitation of the depth information of a scene, that is captured with high accuracy by the light field imaging system. the depth map is estimated both from the original and impaired light field data. then, a similarity measure is applied, and a mapping is performed to link the depth distortion with the perceived quality. Experimental test performed by comparing state-of-art metrics with the proposed one, demonstrate the effectiveness of the proposed metric. © 2019, Society for Imaging Science and Technology.

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image stitching by creating a virtual depth 17

Image stitching by creating a virtual depth

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17th image processing: algorithms and systems conference, ipas 2019

作者： Eid, Ahmed H. Cooper, Brian E. Cholewo, Tomasz J. Lexmark International Inc. LexingtonKY United States

A method for image stitching is presented. the approach focuses on images with parallax (depth variation) to create panoramic views with high fidelity. the approach creates the stitching seam at a virtual depth to convert hard stitching problems to simple ones. the virtual depth is created by applying local distortions to the input images at the stitching seam so that the contents visually appear to be located at the same depth. the presented approach targets a wide variety of applications that require generating high (or super) resolution, wide-view images. these applications include tele-presence (or tele-reality) applications such as shopping, touring, conferencing, planning or architecting, learning, inspection, and surveillance. Our results show that the proposed approach provides promising results compared to commercial products that rely on stitching solutions. © 2019, Society for Imaging Science and Technology.

关键词： Geometrical optics

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Patch-based image despeckling using low-rank hankel matrix approach with speckle level estimation 17

Patch-based image despeckling using low-rank hankel matrix a...

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17th image processing: algorithms and systems conference, ipas 2019

作者： Kim, Hansol Oh, Paul Lee, Sangyoon Kang, Moon G.I. School of Electrical and Electronic Engineering Yonsei University 50 Yonsei-ro Seodaemun-gu Seoul03722 Korea Republic of

In this study, we propose an image despeckling method based on low-rank Hankel matrix approach and speckle level estimation. Annihilating filter-based low-rank Hankel matrix, so called ALOHA approach is very useful to various areas, such as image inpainting and impulse noise reduction. the proposed method utilizes this approach because it provides high performance in completing irregularly subsampled images. Speckled image are subsampled using patch-based speckle level estimator which selects pixels with low speckle level and abandon the others. the subsampled image is reconstructed using low-rank structured matrix completion. Our experimental results demonstrate that precisely estimated speckle level improves despeckling performance significantly. the accuracy of the proposed speckle estimator validates better despeckling performance of the proposed method compared with conventional despeckling methods. © 2019, Society for Imaging Science and Technology.

关键词： Speckle

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A design methodology for hardware acceleration of adaptive filter algorithms in image processing

A design methodology for hardware acceleration of adaptive f...

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17th IEEE International conference on Application-Specific systems, Architectures and Processors

作者： Dutta, Hritam Hannig, Frank Teich, Juergen Heigl, Benno Hornegger, Heinz Univ Erlangen Nurnberg Dept Comp Sci 12 D-8520 Erlangen Germany Siemens AG Med Solut Forchheim Germany

ISBN: (纸本)0769526829

Massively parallel processor array architectures can be used as hardware accelerators for a plenty of dataflow dominant applications. Bilateral filtering is an example of a state-of-the-art algorithm in medical imaging, which falls in the class of 2D adaptive filter algorithms. In this paper we propose a semi-automatic mapping methodology for the generation of hardware accelerators for such a generic class of adaptive filtering applications in image processing. the final architecture deliver similar synthesis results as a hand-tuned design.

关键词： image processing

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