Excessive angular decision diffusion-weighted imaging (HARDWI) is an effective approach for visualizing tissue microstructures, which are otherwise hard to look at the usage of conventional MRI technology. To attain t...
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The proceedings contain 33 papers. The topics discussed include: DWT-RT: a lightweight image deraining model based on discrete wavelet transforms;data-driven optimal traffic signal control with phase priority and swit...
ISBN:
(纸本)9798350308020
The proceedings contain 33 papers. The topics discussed include: DWT-RT: a lightweight image deraining model based on discrete wavelet transforms;data-driven optimal traffic signal control with phase priority and switching cost;sonar object detection based on global context feature fusion and extraction;an image decomposition-based enhancement using a matrix iterative algorithm;tendency coefficient-based weighted distance measure for intuitionistic fuzzy sets with applications;higher-order link prediction based on message passing simplicial networks;short-term power load forecasting based on CEEMDAN-CNN-LSTM hybrid modeling;a method for large scale unconstrained binary quadratic programming problem based on graph neural network;encoding variable stiffness skills with interaction force and motion information for robot-environment interaction;and distributed Nash equilibrium seeking for high-order dynamics with event-triggered communication.
visual information decoding aims to infer the visual content perceived by a subject based on their brain responses, representing a cutting-edge area of neuroscience research. Functional magnetic resonance imaging (fMR...
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The accurate analysis and interpretation of blood vessel images are essential for diagnosing and monitoring various medical conditions. However, these images often suffer from the presence of noise, which can hinder p...
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The accurate analysis and interpretation of blood vessel images are essential for diagnosing and monitoring various medical conditions. However, these images often suffer from the presence of noise, which can hinder proper visualization and lead to erroneous interpretations. In this paper, we present a comprehensive comparative study of noise reduction techniques for blood vessel images, by a literature survey. The study encompasses both traditional and new methods, evaluating their performance, benefits, and challenges. Traditional methods, such as Anisotropic Diffusion Filtering and wavelet Transform, have proven effective in preserving blood vessel structures and retaining fine details. However, they require careful parameter selection and may be computationally intensive. On the other hand, new techniques, including Contrast Limited Adaptive Histogram Equalization (CLAHE), Non-Local Mean Filter (NLM), and deep learning-based approaches, offer promising advancements in noise reduction capabilities with reduced computational complexity. The choice between traditional and new methods depends on specific application requirements, noise characteristics, and available computational resources. Our findings highlight the need for further research in parameter tuning, computational efficiency optimization, and hybrid approaches to enhance the noise reduction process in blood vessel images. This study contributes to the advancement of medical imaging by providing valuable insights for researchers and practitioners, enabling improved diagnostic accuracy and patient care.
Digital signalprocessing has become a fundamental tool in signalprocessingapplications, such as speech and imageprocessing, due to its ability to manipulate and examine signals in a digital form skillfully. A prim...
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This paper comprehensively overviews image and signalprocessing, including their fundamentals, advanced techniques, and applications. imageprocessing involves analyzing and manipulating digital images, while signal ...
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ISBN:
(数字)9798350309249
ISBN:
(纸本)9798350309256
This paper comprehensively overviews image and signalprocessing, including their fundamentals, advanced techniques, and applications. imageprocessing involves analyzing and manipulating digital images, while signalprocessing focuses on analyzing and interpreting signals in various domains. The fundamentals encompass digital signal representation, Fourier analysis, wavelet transforms, filtering, and noise removal. Advanced techniques, such as deep learning for image classification and object detection, are explored. image and signalprocessingapplications include computer vision, medical imaging, audio processing, and communications. This paper is a valuable resource for understanding image and signalprocessing principles and applications, fostering further research and development in these fields.
A method called picture fusion is largely concerned with improving photographs to enhance scene visualization. In order to produce a composite image that is more significant and instructive, it attempts to maintain th...
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The median filter is a simple yet powerful noise reduction technique that is extensively applied in image, signal, and speech processing. It can effectively remove impulsive noise while preserving the content of the i...
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The median filter is a simple yet powerful noise reduction technique that is extensively applied in image, signal, and speech processing. It can effectively remove impulsive noise while preserving the content of the image by taking the median of neighboring pixels;thus, it has various applications, such as restoration of a damaged image and facial beautification. The median filter is typically implemented in one of two major approaches: the histogram-based method, which requires O(1) computation time per pixel when focusing on the kernel radius r, and the sorting-based method, which requires approximately O(r(2)) computation time per pixel but has a light constant factor. These are used differently depending on the kernel radius and the number of bits in the image. However, the computation time is still slow, particularly when the kernel radius is in the mid to large range. This paper introduces novel and efficient median filter with constant complexity O(1) for kernel size using the wavelet matrix data structure, which has been applied to query-based searches on one-dimensional data. We extended the original wavelet matrix to two-dimensional data for application to computer graphics problems. The objective of this study was to achieve high-speed median filter computation in parallel computing environment with many threads (i.e., GPUs). Our implementation for the GPU is an order of magnitude faster than the histogram method for 8-bit images. Unlike traditional histogram methods, which suffer from significant computational overhead, the proposed method can handle images with high pixel depth (e.g., 16- and 32-bit high dynamic range images). When the kernel radius is greater than 12 for 8-bit images, the proposed method outperforms the other median filter computation methods.
Copyright protection of digital images through watermarking technique is one of the viable approaches for industrial and commercial applications. Sustaining a watermark's robustness during attack and preserving th...
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In the field of image denoising, Block Matching 3 Dimensions technique (BM3D) stated as a powerful technique that works to remove white Gaussian noise (A WGN). Despite BM3D shows its great success in comparison with t...
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