Mathematical Imaging
Computational Methods for Image Processing and Analysis
You stand at the intersection of mathematics and visual technology, where abstract equations become powerful tools for understanding and manipulating images. This comprehensive guide takes you through the mathematical foundations that drive modern image processing, from the linear algebra behind basic transformations to the sophisticated optimization techniques used in medical imaging and computer vision. You'll discover how Fourier analysis reveals the hidden frequency structures in images, enabling everything from noise reduction to image compression. The book demonstrates how partial differential equations model natural image processing phenomena, while wavelet theory provides the mathematical framework for multi-resolution analysis. Each concept builds upon the previous, creating a cohesive understanding of how mathematical principles translate into practical imaging solutions. Through clear explanations and computational examples, you'll master the optimization methods that solve inverse problems in image reconstruction, understand the statistical approaches that handle uncertainty and noise, and learn the numerical techniques that make these algorithms computationally feasible. Whether you're working on medical imaging, computer graphics, or digital photography, this book provides the mathematical toolkit you need to tackle complex imaging challenges. By the end, you'll possess both the theoretical understanding and practical knowledge to implement sophisticated imaging algorithms, adapt existing methods to new problems, and contribute to the advancing field of computational imaging.
Est. Length
315 pages (16 chapters)
Language
English
Publication date
Sep 19, 2025
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