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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems VII"

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Asymmetric kernels for dead pixel replacement and cluster masking 33

Asymmetric kernels for dead pixel replacement and cluster ma...

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conference on Infrared Imaging systems - Design, Analysis, Modeling, and Testing XXXIII Part of SPIE Defense and Commercial Sensing conference

作者： Viljoen, Johan W. Goss, Tristan M. Hensoldt South Africa Pty Ltd Optron Div Nellmapius Dr ZA-0157 Irene South Africa

ISBN: (纸本)9781510650893;9781510650886

MWIR FPAs often contain a non-negligible number of dead pixels, and even clusters of pixels. In addition to the undesirable cosmetic effect that these flaws have on the output image, it can also be detrimental to downstream image exploitation efforts, including target detection and tracking algorithms. It is therefore necessary to mask such defects as early as possible, before it is exacerbated by the imaging pipeline processes, especially sharpening filters and contrast stretching, which are commonly used. This paper presents the results of an investigation into several dead-pixel replacement schemes of varying complexity, starting with simple replacement by the previous neighbor to interpolation using kernels of varying shapes and domain sizes. These are evaluated for accuracy, but also for their cluster-handling performance, latency impact, and suitability for real-time implementation on resource-constrained FPGA matrices. It is shown that asymmetric predictive kernels, optimized using neural nets or genetic algorithms, can offer significant improvements over naive last-good-neighbor replacement, while affording improved cluster-handling capability, low FPGA resource requirements, and incurring minimal and even zero latency.

关键词： dead pixel masking NUC image processing kernel integer math

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processing pipeline for automated data mining of the single astronomical objects from blurred CCD frames 12

Processing pipeline for automated data mining of the single ...

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12th International Scientific and Practical conference "Information Control systems and Technologies", ICST 2024

作者： Khlamov, Sergii Savanevych, Vadym Vlasenko, Vladimir Hadzhyiev, Emil Bondar, Yehor Netrebin, Yuriy Kharkiv National University of Radio Electronics Nauki avenue 14 Kharkiv61166 Ukraine National Space Facilities Control and Test Center Moskovska street 8 Kyiv01010 Ukraine INTTVE Limited O'Connell Bridge House D'Olier Street 2 DublinD02RR99 Ireland

In this paper the authors presented a sophisticated data mining pipeline, which was designed for restoring the high-quality images from the blurred frames made by the Charge-Coupled Device (CCD) cameras. The developed data mining pipeline leverages the modern informational technologies for the horizontal and vertical scalability. The core methodology integrates the following mathematical methods and algorithms: an inverse median filtration method for the noise reduction and the Lucy-Richardson algorithm for deblurring. The inverse median filtration effectively reduces impulsive noise while preserving edges, and the Lucy-Richardson algorithm iteratively refines the image by correcting for blurring effects encoded in the point spread function (PSF). The proposed system's architecture of the processing pipeline for automated data mining of the single astronomical objects from blurred CCD frames utilizes the following modern technologies: Python programming language, Redis, FastAPI, React, Docker, and Caddy to ensure high performance, scalability, and ease of deployment. This integrated approach significantly enhances the accuracy of astronomical observations, facilitating more precise studies of celestial objects. The proposed pipeline addresses the unique challenges of astronomical image processing, offering a robust solution for automated data mining of single astronomical objects. Our work demonstrates the potential to advance astronomical research by improving image clarity and reliability, contributing to various fields within astronomy. The combination of effective noise reduction and deblurring techniques, along with a scalable and high-performance system architecture, provides a comprehensive solution to the challenges faced in processing astronomical images. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Noise abatement

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Methodology for the empirical representation of image processing functions in EO/IR sensor system models and simulations 19

Methodology for the empirical representation of image proces...

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conference on Electro-Optical and Infrared systems - Technology and Applications XIX

作者： Hickman, Duncan L. Tektonex Ltd Argyll Rd Kilcreggan Argyll & Bute England

ISBN: (纸本)9781510655461

image processing design plays an important role in the overall performance of modern EO/IR cameras. Incorporating these functions into models and simulators presents a major design challenge, particularly for those cases where complex processing functions are required, such as those found in autonomous ATD/R systems. An established technique involves the use of image-based simulations which process pre- recorded imagery using representative image processing algorithms. However, such an approach requires extensive run times and a large volume of image data, both of which can be prohibitive. An alternative approach is presented here whereby a limited number of images are processed and then used to generate statistically based performance transfer functions utilising an appropriate interpolation scheme. These transfer functions are then used to represent the output response of the processing chain when the received imagery is subjected to different levels of degradations such as distortion and blurring. Such transfer functions can then be stored in multidimensional look-up tables which can be rapidly accessed by a system-level Monte Carlo performance simulation. The ability to represent and extract the performance-related transfer functions is dependent upon the image quality metrics, and the accuracy of the corresponding parametric model requires careful consideration of the model validation. An example simulation is presented based on an autonomous ATD/R sensor system mounted on an airborne platform. The importance of validation is demonstrated, and the increased run-time benefits are described. The proposed parametric image modelling approach provides sensor system designers with increased confidence in their design and compliance, and this helps reduces the early design risk.

关键词： image modelling empirical analysis parametric models image-flow models Monte Carlo simulations model validation ATR

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12th International conference on Recent Trends in Computing, ICRTC 2024

12th International Conference on Recent Trends in Computing,...

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12th International conference on Recent Trends in Computing, ICRTC 2024

ISBN: (纸本)9789819788354

The proceedings contain 34 papers. The special focus in this conference is on Recent Trends in Computing. The topics include: Deep Learning-Based Prediction of Human Ageing Related Proteins;the Insight of Clustering algorithms for Emerging Networking Paradigms;Revolutionizing Cardiovascular Health: Exploring the Potential of AI-Driven Approaches in Diagnosing Arrhythmia Disorders;humor Detection in Telugu Social Media Text Using Cost-Sensitive Learning and Indian Language Embeddings;empirical Comparison of Sleep Disorder Prediction Using Machine Learning Techniques;securing Smart Grids: Decentralized Anomaly Detection Using Federated Learning and Recurrent Neural Networks;machine Learning-Powered Intrusion Detection for Network Security;secure Chat System: Harnessing the Power of Hybrid Encryption for Enhanced Security and Confidentiality;deep Neural Networks for Early Diabetes Mellitus Prediction;advancements in image Deblurring and Performance Metrics Using Deep Learning Technique;emotion-Levitating Music Recommendation System Through Real-Time Facial Recognition;smartVoice TrashMate: The Intelligent Voice-Controlled Trash Bin;an Innovative image Enhancement Algorithm for Color Filter Array images: A Novel Approach;Enhancing Secure and Efficient Communication in Free-Space Optical Networks Using Spectral Amplitude Coding - OCDMA Technology;enhancing Citrus Fruit Classification and Quality Assessment Through Advanced Machine Learning Techniques;connecting Health for a Better Tomorrow Through Internet of Medical Things;image processing in Retail Marketing: Innovations for Customer Engagement, Sales, and Operational Efficiency;Leaf Checker: AI-Based Plant Diagnostic Tool Using Alexnet and Mobilenet;ioT-Supported Indoor Navigation System Using Machine Learning Technique;a Customizable Virtual Assistant Enhanced with Vision and Speech Capabilities;An Unequal Cluster-Based Routing Mechanism Using Lyrebird Optimization Algorithm to Achieve Network Longevity in WSNs.

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Medical image Noise Reduction Methods Based on Cutting-Edge Hybrid Filtering Designs

Medical Image Noise Reduction Methods Based on Cutting-Edge ...

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International conference on Smart systems and Wireless Communication, SSWC 2024

作者： Saha, Tanusree Vishal, Kumar Magadh University Bodh Gaya India Department of Information Technology JIS College of Engineering Kalyani India Department of Mathematics Magadh University Bodh Gaya India

ISBN: (纸本)9789819613472

A high-resolution noise free medical image is an essential requirement for accurate disease detection. Medical image processing and analysis techniques have become utmost important in recent decades as a supplementary competence for medical professionals in the fields of diagnosis and prevention. Obtaining high-quality images without any interference is a crucial requirement for effectively managing therapeutic images. Medical images may contain many types of noises for involvement of simultaneous operation of multiple equipment involved in data capturing and transmission. Various filters have been suggested for eliminating different types of noises, each having its own merits and drawbacks depending on its capabilities and projected benefits. Our research depicts a technique for removing noises from medical images using a hybrid filtering approach based on ensemble adaptive mean filtering and Block-matching-3D filtering. The proposed methodologies specifically aim to reduce the speckle noise in brain MRI images obtained from ultrasonic therapy by employing hybrid filtering techniques. This proposed hybrid filtering algorithms integrate the most effective aspects of high-performance filters to achieve a de-noised image that retains fine details, edges, and textures. The statistical performance measurement metrics like Root Mean Square Error (RMSE), Peak Signal-to-Noise Ratio (PSNR) are employed to evaluate the improved visual quality of the images. The results indicate that our proposed hybrid filtering technique functioning efficiently and provide better quality de-noised image compare with other existing de-noising techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image enhancement

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12th International conference on Recent Trends in Computing, ICRTC 2024

12th International Conference on Recent Trends in Computing,...

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12th International conference on Recent Trends in Computing, ICRTC 2024

ISBN: (纸本)9789819789450

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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

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.

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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Diffusion models as plug-and-play priors 36

Diffusion models as plug-and-play priors

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36th conference on Neural Information processing systems (NeurIPS)

作者： Graikos, Alexandros Malkin, Nikolay Jojic, Nebojsa Samaras, Dimitris SUNY Stony Brook Stony Brook NY 11794 USA Univ Montreal Mila Montreal PQ Canada Microsoft Res Redmond WA USA

ISBN: (纸本)9781713871088

We consider the problem of inferring high-dimensional data x in a model that consists of a prior p(x) and an auxiliary differentiable constraint c(x, y) on x given some additional information y. In this paper, the prior is an independently trained denoising diffusion generative model. The auxiliary constraint is expected to have a differentiable form, but can come from diverse sources. The possibility of such inference turns diffusion models into plug-and-play modules, thereby allowing a range of potential applications in adapting models to new domains and tasks, such as conditional generation or image segmentation. The structure of diffusion models allows us to perform approximate inference by iterating differentiation through the fixed denoising network enriched with different amounts of noise at each step. Considering many noised versions of x in evaluation of its fitness is a novel search mechanism that may lead to new algorithms for solving combinatorial optimization problems. The code is available at https://***/AlexGraikos/diffusion_priors.

关键词： image segmentation

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Exploring Data as Quantum Bits: Analysis of Quantum image Encoding Patterns for NISQ era

Exploring Data as Quantum Bits: Analysis of Quantum Image En...

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2023 IEEE International conference on Quantum Technologies, Communications, Computing, Hardware and Embedded systems Security, iQ-CCHESS 2023

作者： Gopakumar, Parvathy Rubell Marion Lincy, G. Indian Institute of Information Technology Kottayam Department of Computer Science and Engineering Kerala Pala Kottayam India

ISBN: (纸本)9798350314946

The generall efficiency of a computational system, be it simple or complex, presentation of the data plays a crucial role in getting the most out of it. Classical information theory, quantum mechanics, and computer science collectively influence Quantum Information in comprehending how information can be encoded, transmitted, and modified using quantum systems. Quantum mechanics allows quantum information to incorporate a variety of properties, such as quantum encoding of classical information, storing coherent details in quantum superpositions, quantum computations and entanglement, which occurs when two or more qubits become highly correlated and share an unbreakable quantum state. There are numerous barriers and constraints in the broad field of quantum computation, ranging from dealing with Decoherence to ensuring error correction. Moreover, the qubit, a fundamental computational unit, is challenging to create, manipulate, and store. To ensure the quality of the Quantum algorithms, the area of quantum data encoding has been thoroughly researched and explored as relevant results can be obtained by adequate encoding. In this paper, we reviewed the terminology and physical modeling of qubits, as well as the encoding approaches used to transform data from classical to quantum models. Furthermore, different encoding schemes for Quantum image processing were explored to determine the efficiency of quantum pixel representation(QPIXL) framework for effective application in NISQ era. © 2023 IEEE.

关键词： Qubits

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Deep Learning-Based Diagnosis of Rice Sheath Rot Disease: A Wavelet-Filtered Approach for Feature Extraction and Analysis

Deep Learning-Based Diagnosis of Rice Sheath Rot Disease: A ...

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2024 International conference on Knowledge Engineering and Communication systems, ICKECS 2024

作者： Atheeswaran, Athiraja Maheshkumar, K. Jayareka, K.S. Janani, B. Lakshmanan, K. Sobiyaa, P. Malla Reddy College of Engineering Department of Cse Telangana Hyderabad India Bannari Amman Institute of Technonology Dept of Computer Science and Design Tamilnadu Erode India Sona College of Technology Dept of Computer Science and Engineering Tamilnadu Salem India Dr.NGP Institute of Technology Department of Computer Science and Business Systems Tamilnadu Coimbatore India Hindusthan College of Engineering and Technology Dept of Cse Tamilnadu Coimbatore India Bannari Amman Institute of Technonology Dept of Information Technology Tamilnadu Erode India

ISBN: (纸本)9798350359688

A major danger to rice farming, rice sheath rot disease affects the quantity and quality of one of the main food crops grown worldwide. This research uses a large dataset gathered from many sources, such as agricultural universities, research centres, and first hand field observations, to provide a unique method of diagnosing Rice Sheath Rot disease using cutting-edge deep learning algorithms. Our work is primarily focused on creating a dependable and effective diagnostic tool using state-of-the-art image processing and machine learning methods. Wavelet filters are applied in the first step of our approach to pre-process photos. This method is essential for eliminating noise and improving the quality of the dataset, which is made up of high-definition photos of rice plants with the disease Sheath Rot. The colour, shape, and texture of the sick spots are then analyzed using advanced feature extraction techniques. When separating plant tissues that are diseased from those that are not, these characteristics are essential. Convolutional Neural Networks (CNN), our primary diagnostic model's foundation, were selected due to their shown efficacy in picture recognition and classification applications. The preprocessed and feature-enhanced dataset is used to train the CNN model, giving it the ability to recognize the complex patterns and traits connected to Sheath Rot illness. The architecture of the model is built to maximize efficiency and accuracy, guaranteeing quick and accurate illness identification. By utilizing AI and deep learning, the proposed method seeks to not only help farmers and agronomists identify diseases early on but also make a substantial contribution to the area of agricultural disease diagnostics. Combine that our method, which combines CNN-based analysis with wavelet-filtered pre-processing establishes a new standard for the identification and treatment of Rice Sheath Rot disease. This might result in improved crop management techniques and more pot

关键词： image enhancement

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