Details
Artificial Intelligent Algorithms for Image Dehazing and Non-Uniform Illumination Enhancement
Algorithms for Intelligent Systems
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181,89 € |
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| Verlag: | Springer |
| Format: | |
| Veröffentl.: | 17.06.2024 |
| ISBN/EAN: | 9789819720118 |
| Sprache: | englisch |
Dieses eBook enthält ein Wasserzeichen.
Beschreibungen
<p>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.</p>
<p>Introduction.- Literature Survey.- Modified Transmission Map Estimation Function.- Compact Single Image Dehazing Network.- Z-Score Approach for Image Enhancement.</p>
<p>Dr. Teena Sharma is an Assistant Professor at the Mehta Family School of Data Science and Artificial Intelligence (MFSDS&AI), Indian Institute of Technology Guwahati, India. Dr. Sharma worked as a Postdoctoral Scholar at the University of Tennessee, Memphis, Tennessee, USA. Dr. Sharma received her Ph.D. degree in Electrical Engineering from the Indian Institute of Technology Kanpur, India. Dr. Sharma's research interests are Artificial Intelligence, Machine Learning, and Deep Learning Algorithms and their applications to Computer Vision: Object detection, Classification, Identification, Recognition, Image enhancement, Image matching; Equitable Precision Medicine: Transfer learning, Meta-learning, Few-shot learning; and Condition-based Monitoring: Fault diagnosis and Remaining useful life prediction. Dr. Sharma is also serving as an Associate Editor for the IEEE Transactions on Artificial Intelligence.</p>
<p>Dr. Nishchal K Verma (SM'13) is a Professor in the Department of Electrical Engineering at the Indian Institute of Technology Kanpur, India. He obtained his Ph.D. in Electrical Engineering from the Indian</p>
<p>Institute of Technology Delhi, India. Dr. Verma's research expertise falls under Artificial Intelligence (AI) related theories and its applications to many inter-disciplinary domains but not limited to machine learning, deep learning, computer vision, prognosis and health management, bioinformatics, cyber-physical systems, complex and highly non-linear systems modeling, etc. He has published more than 250 research papers and 4 Books (edited/ co-authored) in the field of AI. He has successfully completed 23 projects from various funding agencies such as The BOEING Company, USA, DST, DRDO, JCBCAT, MHRD, SERB, CSIR, IIT Kanpur, MCIT, SFTIG, VTOL, etc. He has been serving as Associate Editor of IEEE Transactions on Artificial Intelligence and IEEE Transactions on Neural Networks and Learning Systems.</p>
<p>Dr. Nishchal K Verma (SM'13) is a Professor in the Department of Electrical Engineering at the Indian Institute of Technology Kanpur, India. He obtained his Ph.D. in Electrical Engineering from the Indian</p>
<p>Institute of Technology Delhi, India. Dr. Verma's research expertise falls under Artificial Intelligence (AI) related theories and its applications to many inter-disciplinary domains but not limited to machine learning, deep learning, computer vision, prognosis and health management, bioinformatics, cyber-physical systems, complex and highly non-linear systems modeling, etc. He has published more than 250 research papers and 4 Books (edited/ co-authored) in the field of AI. He has successfully completed 23 projects from various funding agencies such as The BOEING Company, USA, DST, DRDO, JCBCAT, MHRD, SERB, CSIR, IIT Kanpur, MCIT, SFTIG, VTOL, etc. He has been serving as Associate Editor of IEEE Transactions on Artificial Intelligence and IEEE Transactions on Neural Networks and Learning Systems.</p>
<p>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.</p>
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 enhancement Includes experiments with several real-time datasets for vision-based algorithms
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