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

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

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

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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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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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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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Comparative image processing and Object Detection in Aerial Search and Rescue

Comparative Image Processing and Object Detection in Aerial ...

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Cognitive Robotics and Intelligent systems (ICC - ROBINS), International conference on

作者： Sushil Rawat Abhay Singh Rawat vikas Singh Mewad Himadri Vaidya Department of Computer Science and Engineering Graphic Era Hill University Dehradun India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

Aerial search and response plays an important role in finding and rescuing persons in need. Unmanned Aerial Vehicle (UAV) -acquired aerial images provide an intensive profile search area and facilitate identification of potential targets. To improve the effectiveness of aircraft Search and Rescue missions, this study compares many object detection algorithms and image processing methods. The investigated object detection methods are Mask R-CNN, SSD, and YOLOv8, and image processing techniques are contrast improvement and image brightness control. The effectiveness of these strategies has been evaluated by using real aerial images that include different targets such as humans, vehicles, and fire. The results show how well the object detection algorithms work to accurately distinguish targets, and how an attractive image processing approach improves the image performance of the best object detection model, as well as how the performance of the best object detection model with image processing can improve the performance of model. This research shows that combining image processing (contrast and brightness) with an optimal object detection algorithm (Mask R-CNN) significantly improves target detection in aerial search and rescue. While only Mask R-CNN performs best, adding image processing increases its accuracy to 0.92, mAP to 0.89, and makes it more reliable in finding people in need. This shows the potential of this joint approach to save people in air rescue missions.

关键词： image quality image processing Brightness Object detection Search problems Autonomous aerial vehicles Real-time systems

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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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Optimization of Micro-object Identification Based on the Mellin Transform and the Use of Parallel Computing

Optimization of Micro-object Identification Based on the Mel...

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2023 International Russian Smart Industry conference, SmartIndustryCon 2023

作者： Jumanov, Isroil I. Kholmonov, Sunatillo M. Samarkand State University Intelligent Systems and Computer Technologies Samarkand Uzbekistan

ISBN: (纸本)9781665464291

Scientific and methodological foundations for the optimal identification of non-stationary objects based on the use of neural networks have been developed. Models and algorithms for detection, extraction of hidden relationships, useful properties and patterns in data, formation of a database and knowledge bases are proposed. Mechanisms have been developed for using the statistical, dynamic and specific characteristics of images, unique features of three, five-layer neural networks and combined models for setting variables with typical recognition and classification tools. Have been developed computational schemes for determining and adjusting the weights of neurons, choosing a suitable activation function, coefficients of synaptic and interneuronal connections, rational neural network architecture, the number of layers and neurons in the layers of the network, a set of functions of nonlinear dependencies "inputs-outputs". Data pre-processing algorithms are implemented that perform the functions of informative features selection, segmentation, object image contour extraction, search based on methods with annealing, prohibition, and stochastic search. Tested neural networks of Hopfield, Hamming, Hebb, Kohonen, bidirectional associative memory were tested. Schemes for two and three-dimensional image reconstruction based on the synthesis of tools for calculating Mellin transform functions, initial values of centroids, and the formation of a suboptimal set of variables are proposed. The identification software package in C++ was developed and implemented in the CUDA parallel computing environment. © 2023 IEEE.

关键词： Extraction

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