Quaternion-Based Sparse Image Processing

Quaternion-Based Sparse Image Processing by Satya Prakash Yadav

Capture the frontier of multi-channel image analysis with this authoritative guide that translates advanced mathematics into practical imaging solutions. Combining quaternion algebra with modern sparse-representation techniques, this book reveals powerful methods for color image denoising, reconstruction, feature extraction, and compressed sensing that outperform traditional scalar approaches.

Packed with clear explanations and illustrative case studies, the text walks readers through quaternion fundamentals, transform-domain sparse modeling, and algorithmic strategies for real-world tasks in remote sensing, medical imaging, robotics, and computer vision. Emphasizing interpretability and computational efficiency, it presents robust approaches to preserve inter-channel correlation in RGB, multispectral, and hyperspectral imagery — essential for researchers and practitioners across India, the USA, Europe, and the Asia-Pacific region.

Whether you are a graduate student, research scientist, or engineer, you’ll gain practical insight into designing quaternion-based filters, sparse coding schemes, and reconstruction pipelines that scale to large datasets. The author’s balanced blend of theory, algorithmic detail, and application-driven examples makes complex concepts accessible without sacrificing rigor.

Advance your imaging projects with techniques that harness color structure and sparsity for higher fidelity results. Ideal for academic courses, R&D labs, and industry teams working in geospatial analysis, medical diagnostics, and autonomous systems. Order your copy today to bring quaternion-based sparse image processing into your toolkit and stay at the cutting edge of multi-channel image analysis.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.