Quaternion-Based Sparse Image Processing: Advances in Multispectral Processing
Quaternion-Sparse Image Processing: Advances in Multispectral Processing brings together the technologies, research, and managerial applications of quaternion-sparse based complex algebra in image processing. The book covers the entire range of complicated tasks performed on color images, including denoising, reconstruction, classification, hallucination, feature extraction, dimension reduction, and regularization. It provides easy understanding and smooth adaptability of basic and advanced concepts for graduate students, researchers, doctors, academics, and practitioners. - Uncovers the innovative features of complex algebra, specifically the quaternion-sparse concept in image processing and how it can help in improving the computational efficiency of image processing - Deals with the most common quaternion convolution neural network, quaternion wavelet, and sparse representation-based techniques in multispectral image processing - Focuses on how evolution in algebraic concepts, i.e., quaternion and sparse, help in improving accuracy and efficiency of various color image restoration, reconstruction, and recognition - Illustrates how important features are extracted and complete information is stored in extracted features to help and process tasks in an easy and computationally efficient way
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Quaternion-Based Sparse Image Processing: Advances in Multispectral Processing
Quaternion-Sparse Image Processing: Advances in Multispectral Processing brings together the technologies, research, and managerial applications of quaternion-sparse based complex algebra in image processing. The book covers the entire range of complicated tasks performed on color images, including denoising, reconstruction, classification, hallucination, feature extraction, dimension reduction, and regularization. It provides easy understanding and smooth adaptability of basic and advanced concepts for graduate students, researchers, doctors, academics, and practitioners. - Uncovers the innovative features of complex algebra, specifically the quaternion-sparse concept in image processing and how it can help in improving the computational efficiency of image processing - Deals with the most common quaternion convolution neural network, quaternion wavelet, and sparse representation-based techniques in multispectral image processing - Focuses on how evolution in algebraic concepts, i.e., quaternion and sparse, help in improving accuracy and efficiency of various color image restoration, reconstruction, and recognition - Illustrates how important features are extracted and complete information is stored in extracted features to help and process tasks in an easy and computationally efficient way
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Quaternion-Based Sparse Image Processing: Advances in Multispectral Processing

Quaternion-Based Sparse Image Processing: Advances in Multispectral Processing

Quaternion-Based Sparse Image Processing: Advances in Multispectral Processing

Quaternion-Based Sparse Image Processing: Advances in Multispectral Processing

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Overview

Quaternion-Sparse Image Processing: Advances in Multispectral Processing brings together the technologies, research, and managerial applications of quaternion-sparse based complex algebra in image processing. The book covers the entire range of complicated tasks performed on color images, including denoising, reconstruction, classification, hallucination, feature extraction, dimension reduction, and regularization. It provides easy understanding and smooth adaptability of basic and advanced concepts for graduate students, researchers, doctors, academics, and practitioners. - Uncovers the innovative features of complex algebra, specifically the quaternion-sparse concept in image processing and how it can help in improving the computational efficiency of image processing - Deals with the most common quaternion convolution neural network, quaternion wavelet, and sparse representation-based techniques in multispectral image processing - Focuses on how evolution in algebraic concepts, i.e., quaternion and sparse, help in improving accuracy and efficiency of various color image restoration, reconstruction, and recognition - Illustrates how important features are extracted and complete information is stored in extracted features to help and process tasks in an easy and computationally efficient way

Product Details

ISBN-13: 9780443292453
Publisher: Elsevier Science
Publication date: 07/02/2025
Sold by: Barnes & Noble
Format: eBook
Pages: 400
File size: 44 MB
Note: This product may take a few minutes to download.

About the Author

Dr. Satya Prakash Yadav (FIETE & SMIEEE) is currently the Associate Professor of the Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, U.P. , India and has completed his Postdoctoral Research Fellow from Federal Institute of Education, Science and Technology of Ceará, Brazil. He has awarded his PhD degree from Dr. A.P.J. Abdul Kalam Technical University (AKTU) (formerly UPTU). Currently, 4 students are working for Ph.D. under his guidance. A seasoned academician having more than 18 years of experience, he has published four books (Programming in C, Programming in C++ and Blockchain and Cryptocurrency) under I.K. International Publishing House Pvt. Ltd. Including Distributed Artificial Intelligence: A Modern Approach, Published December 18, 2020 by CRC Press. He has undergone industrial training programs during which he was involved in live projects with companies in the areas of SAP, Railway Traffic Management Systems, and Visual Vehicles Counter and Classification (used in the Metro rail network design). He is an alumnus of Netaji Subhas Institute of Technology (NSIT), Delhi University. A prolific writer, Dr. Satya Prakash Yadav has published six patents and authored many research papers in web of science indexed journals. Additionally, His area of specialisation is in the areas of Image Processing, Information retrieval and Features extraction. Also, he is a Editor in Chief in Journal of Cyber Security in Computer System & Journal of Soft Computing and Computational Intelligence (MAT journals), Series Editor in DeGruyter International Publisher, Bentham Science and CRC Press, Taylor and Francis Group Publisher (U.S.A).
Pethuru Raj PhD works as chief architect and vice president of site reliability engineering (SRE) division of Reliance Jio Infocomm. Ltd. Bangalore. Previously he worked as a cloud infrastructure architect in the IBM Global Cloud Center of Excellence (CoE), Bangalore. He worked as a TOGAF-certified enterprise architecture (EA) consultant in Wipro Consulting Services (WCS) Division and as a lead architect in the corporate research (CR) division of Robert Bosch, India. He has gained more than 18 years of IT industry experience. He finished the CSIR-sponsored PhD degree in Anna University, Chennai and continued the UGC-sponsored postdoctoral research in the department of Computer Science and Automation, Indian Institute of Science, Bangalore. Thereafter, he was granted a couple of international research fellowships (JSPS and JST) to work as a research scientist for 3.5 years in two leading Japanese universities. He has authored and edited 18 books thus far and he focuses on some of the emerging technologies such as Containerized Clouds; Big, Fast, and Streaming Data Analytics; Microservices architecture (MSA); Machine and Deep Learning Algorithms; Blockchain Technology; The Internet of Things; and Edge Computing. He has published more than 30 research papers in peer-reviewed journals such as IEEE, ACM, Springer-Verlag, Inderscience, etc.
Prof. Dr. Fadi Al-Turjman received his Ph.D. in computer science from Queen’s University, Canada, in 2011. He is the “advisor to the chairman of the board of trustees on AI and informatics” at Near East University (NEU), Turkey, and the founding dean for AI and Informatics faculty in the same university. Prof. Al-Turjman is the head of Software Engineering Department, and the founding director for the AI and Robotics Institute and the International Research Center for AI and IoT at NEU. He has been awarded the lifetime golden-award of Dr. Suat Gunsel from Near East University for the year 2022, in addition to several other recognitions and best research awards at key international venues. Prof. Al-Turjman is a leading authority in the areas of smart/intelligent IoT systems, wireless, and mobile networks’ architectures, protocols, deployments, and performance evaluation in Artificial Intelligence of Things (AIoT). His publication history spans over 650 SCI/E publications, in addition to numerous keynotes and plenary talks at flagship venues. He has authored and edited more than 100 books about AI, cognition, security, and wireless sensor networks’ deployments in smart IoT environments, which have been published by well-reputed publishers such as IEEE, Taylor and Francis, Elsevier, IET, and Springer. Prof. Al-Turjman is leading a number of international conferences and workshops in flagship AI and IoT societies. Currently, he serves as a book series editor with distinguished publishers, and the lead guest/associate editor for several top tier journals, including the IEEE Communications Surveys and Tutorials (IF 23.9) and the Elsevier Sustainable Cities and Society (IF 10.8).
Victor Hugo C. de Albuquerque [M’17, SM’19] is a collaborator Professor and senior researcher at the Graduate Program on Teleinformatics Engineering at the Federal University of Ceará, Brazil, and at the Graduate Program on Telecommunication Engineering, Federal Institute of Education, Science and Technology of Ceará, Fortaleza/CE, Brazil. He has a Ph.D in Mechanical Engineering from the Federal University of Paraíba (UFPB, 2010), an MSc in Teleinformatics Engineering from the Federal University of Ceará (UFC, 2007), and he graduated in Mechatronics Engineering at the Federal Center of Technological Education of Ceará (CEFETCE, 2006). He is a specialist, mainly, in Image Data Science, IoT, Machine/Deep Learning, Pattern Recognition, Robotic.
Dr. Sudesh Yadav is an Assistant Professor (Computer Science) in Dept. of Higher Education Haryana. She is currently working at Govt. College, Ateli, Indira Gandhi University Meerpur, Rewari, Haryana. She has awarded her M.Tech. Degree in Information Security with distinction from Guru Gobind Singh Indraprastha University. She has done her Ph. D. in Computer Science and Engineering from Guru Gobind Singh Indraprastha University Delhi with fellowship. A prolific writer, Dr. Sudesh Yadav has authored many research papers in web of science indexed journals. Additionally, she has presented research papers at many international conferences and seminars in the areas of Image Processing, Information retrieval, Features extraction and Programming, artificial intelligence, IoT, digital image processing, soft computing and pattern recognition, natural language processing. She has reviewed many research papers of international journals and conferences and seminars. Her area of interest is artificial intelligence, digital image processing, feature extraction, sparse representation, internet of things, internet of medical things, information retrieval, data science etc.

Table of Contents

1. Quaternion-Sparse based image processing: Applications, benefits and future challenge2. Technical challenges in using quaternion complex algebra for multimodal and Multispectral image processing3. Quaternion differential equations for multimodal and multispectral color image processing4. Quaternion cooler sparse representations for image processing: a technical review5. Quaternion sparse matrix optimization6. Collaborative and sparse representation of color image processing7. Robust sparse representation in quaternion space for multimodal image processing8. Sparse representation-based regularization for multimodal and multispectral image processing9. Fusion of multimodal, multispectral image processing using Quaternion Principal Component Analysis10. Experimental evaluation and validation of different variants of PCA on quaternion multispectral imaging11. Quaternion wavelet transformation for image processing: application, benefits and future challenges12. Discrete wavelet transformation on quaternion space13. De-noising in color images using quaternion discrete wavelet transformation14. Fundamentals of hypercomplex Fourier transform in quaternion space15. Application of Hypercomplex Fourier transform in quaternion space for skin image classification16. Quaternion neural network types of application, benefits and future challenges for multispectral image processing17. Quaternion neural network for geometrical operators in high dimensional quaternion space18. Image dehazing using quaternion complex algebra-based neural networks19. Classification of multispectral imaging using quaternion convolution neural network20. Phasor quaternion neural network for multimodal image processing

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Discusses advancements in image processing and how quaternion-sparse image processing techniques can be developed and deployed in industry and academia

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