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Titlebook: Artificial Intelligent Algorithms for Image Dehazing and Non-Uniform Illumination Enhancement; Teena Sharma,Nishchal K. Verma Book 2024 Th

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發(fā)表于 2025-3-21 17:13:48 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Artificial Intelligent Algorithms for Image Dehazing and Non-Uniform Illumination Enhancement
影響因子2023Teena Sharma,Nishchal K. Verma
視頻videohttp://file.papertrans.cn/168/167611/167611.mp4
發(fā)行地址Provides an excellent starting point for all those interested in AI based image enhancement.Provides an elaborate review of literature discussing image dehazing and non-uniform illumination enhancemen
學(xué)科分類Algorithms for Intelligent Systems
圖書封面Titlebook: Artificial Intelligent Algorithms for Image Dehazing and Non-Uniform Illumination Enhancement;  Teena Sharma,Nishchal K. Verma Book 2024 Th
影響因子.This book offers a detailed insight of artificial intelligence (AI) algorithms for image dehazing and non-uniform illumination enhancement. In this book, various image enhancement techniques under hazy and non-uniform illumination conditions are discussed. The book specifically provides a detail on how to approach image enhancement under different outdoor conditions using AI tools. The biggest benefit a reader would accrue is to get exposed to the various aspects one should take care of while working with digital images. The book also includes multiple inventions which were recently introduced by the authors for image enhancement and reviews the state of the art in respective subject matters..
Pindex Book 2024
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Introduction,ge is characterized by various visible elements, including illumination, color, and gradients. Unfortunately, factors such as blur, noise, and block artifacts near edges can significantly impact these visible aspects, leading to a decline in the efficacy of computer vision-based decision-making algo
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Z-Score Method,th a focus on applications in multi-media. The presented approach involves estimating scene transmission through a .-score based weighting function and utilizing global atmospheric light for image dehazing. In contrast, the method equalizes the illumination channel by employing the .-score weighting
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發(fā)表于 2025-3-22 20:05:07 | 只看該作者
Image Dehazing Using Type-2 Fuzzy Approach,timating the depth map and global atmospheric light from the observed hazy image. Subsequently, the estimated depth map is used to generate the true scene transmission. Finally, the observed hazy image undergoes dehazing through the atmospheric scattering model, utilizing the scene transmission and
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Special Transfusion Situations,mages produced by CSIDNet successfully strike a balance between speed and accuracy. The chapter presents the results of CSIDNet highlighting the potential application of CSIDNet in critical and real-time scenarios.
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