The propagation of digital content and its ease of distribution over the internet have raised concerns regarding the protection of intellectual property and its prevention from unauthorized usage. One such effective m...
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This paper delves into the groundbreaking potential of quantum computing, with a primary focus on qubits and their wide-ranging applications. It explores the fundamental principles of quantum physics, such as entangle...
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Recently, machine learning algorithms have been widely used in the fields of imageprocessing, network security and natural language processing, etc., profoundly affecting human life. However, machine learning algorit...
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Multimodal image fusion aims to merge features from different modalities to create a comprehensively representative image. However, existing medical image fusion methods often struggle to handle noise generated during...
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The travel industry is undergoing profound changes with the continuous development of information technology. One such emerging technology is cloud computing, which boasts powerful computing power, massive storage spa...
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ISBN:
(数字)9798350360240
ISBN:
(纸本)9798350384161
The travel industry is undergoing profound changes with the continuous development of information technology. One such emerging technology is cloud computing, which boasts powerful computing power, massive storage space, and efficient resource sharing capabilities. Based on Ali-Cloud's ESC and Baidu-Cloud's EasyDL cloud computing platform, this paper focuses on the definition, functions and architecture of cloud computing to explore how to design a rural smart tourism system to assist the development of rural tourism.
Telehealth applications, such as remote diagnosis and examination, have become more and more popular nowadays. However, the generated large number of medical images and their impractical digital size when it comes to ...
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ISBN:
(纸本)9781665491303
Telehealth applications, such as remote diagnosis and examination, have become more and more popular nowadays. However, the generated large number of medical images and their impractical digital size when it comes to telehealth have brought pressure on communication infrastructures. More specifically, the imageprocessing time has increased dramatically due to the size of the digitized images, while requiring substantial transmission bandwidth and storage space. For these reasons, medical image compression has become a hot research topic. While image compression techniques have evolved over several generations, it remains an open question how these standards will perform when it comes to sensitive oversized medical image content. In this paper, the emerging compression standard High Efficiency Video Coding (HEVC) and the next generation standard Versatile Video Coding (VVC) are evaluated on two different medical image datasets, covering the entire range of quality levels. The results of this study show that the VVC standard overperforms HEVC on raw medical image compression both visually and quantitatively, with the performance gap narrowing when dealing with pre-compressed medical images.
The research of computer intelligent image recognition algorithm and technology is the research field of the development of computer intelligent image recognition algorithm. The main purpose of this research field is ...
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With the acceleration of the global informatization process, improving the efficiency of information processing has become a top priority. The development of science and technology has made the information processing ...
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Recent advancements in deep generative models have facilitated the creation of photo-realistic images across various tasks. However, these generated images often exhibit perceptual artifacts in specific regions, neces...
ISBN:
(纸本)9798350307184
Recent advancements in deep generative models have facilitated the creation of photo-realistic images across various tasks. However, these generated images often exhibit perceptual artifacts in specific regions, necessitating manual correction. In this study, we present a comprehensive empirical examination of Perceptual Artifacts Localization (PAL) spanning diverse image synthesis endeavors. We introduce a novel dataset comprising 10,168 generated images, each annotated with per-pixel perceptual artifact labels across ten synthesis tasks. A segmentation model, trained on our proposed dataset, effectively localizes artifacts across a range of tasks. Additionally, we illustrate its proficiency in adapting to previously unseen models using minimal training samples. We further propose an innovative zoom-in inpainting pipeline that seamlessly rectifies perceptual artifacts in the generated images. Through our experimental analyses, we elucidate several invaluable downstream applications, such as automated artifact rectification, non-referential image quality evaluation, and abnormal region detection in images. The dataset and code are released here: https://***/PAL4VST
In this research, we provide a method for encrypting digital images that is based on DNA block encoding and multi-chaotic systems. First, create a scrambling matrix using the one-dimensional logistic chaotic map, then...
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