This book uncovers the innovative features of complex algebra i.e., the octonion -sparse concept in image processing and how it can help in improving the computational efficiency of image processing.
It deals with the most common octonion convolution neural network, quaternion wavelet, and sparse representation-based techniques in multispectral image processing.
The book focusses on how evolution in algebraic concepts i.e. octonion and sparse helps in improving accuracy and efficiency of various color image restoration, reconstruction and recognition.
It provides examples of research and real-world applications related to biometric security, medical image processing, face recognition, and how crucial characteristics are extracted.
The main focus of this book is to bring all the related technologies, novel findings and managerial applications of Colored Medical Imaging on a single platform to provide great readability, easy understanding, and smooth adaptability of various basic and advanced concepts to the Undergraduate and Postgraduate students, Researchers, Managers, Designers, Academicians, and Practitioners doing work in this domain.

eBook - PDF
Octonion Sparse-Based Image Processing
Multispectral Imaging and Feature Extraction
- 376 pages
- English
- PDF
- Available on iOS & Android
eBook - PDF
Octonion Sparse-Based Image Processing
Multispectral Imaging and Feature Extraction
About this book
Table of contents
- Preface
- Contents
- List of authors
- 1 Introduction to octonion sparse medical image process
- 2 Octonion sparse mathematics: theory and practice
- 3 Octonion sparse representation of multispectral innovative stereoimage classification
- 4 Octonion-sparse-based image processing: applications, benefits, and future challenges
- 5 Technical challenges in using octonion complex algebra for multimodal and multispectral image processing
- 6 Octonion differential equations for multimodal and multispectral color image processing
- 7 Octonion cooler sparse representations for image processing: a technical review
- 8 Octonion sparse matrix optimization
- 9 Collaborative and sparse representation of color image processing
- 10 Robust sparse representation in octonion space for multimodal image processing
- 11 Sparse representation-based regularization for multimodal and multispectral image processing
- 12 Fusion of multimodal, multispectral image processing using octonion principal component analysis
- 13 Experimental evaluation and validation of different variants of PCA on octonion multispectral imaging
- 14 Octonion wavelet transformation for image processing: application, benefits, and future challenges
- 15 Discrete wavelet transformation on octonion space
- 16 De-noising in color images using octonion discrete wavelet transformation
- 17 Fundamentals of hypercomplex Fourier transform in octonion space
- 18 Application of hypercomplex Fourier transform in octonion space for skin image classification
- 19 Octonion neural network: types of applications, benefits, and future challenges for multispectral image processing
- 20 Octonion neural network for geometrical operators in high-dimensional quaternion space
- 21 Image dehazing using octonion complex algebra-based neural networks
- 22 Classification of multispectral imaging using octonion convolution neural network
- 23 Optimizing top precision performance measure of content-based image retrieval by learning similarity function
- 24 Phasor octonion neural network for multimodal image processing
- Index
- Mathematical Methods in the Digital Age
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Information
Publisher
De GruyterEdition
1Year
2025Topic
MathematicsPrint ISBN
9783119146661
Subtopic
Information TechnologyeBook ISBN
9783112208076
Index
MathematicsFrequently asked questions
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Yes, you can access Octonion Sparse-Based Image Processing by Satya Prakash Yadav,Abhay Bansal,Victor Hugo C. de Albuquerque,Fadi Al-Turjman,Houbing Herbert Song in PDF and/or ePUB format, as well as other popular books in Mathematics & Information Technology. We have over 1.5 million books available in our catalogue for you to explore.