Markus Haltmeier – författare
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3 produkter
3 produkter
Inbunden, Engelska, 2008
540 kr
Skickas inom 10-15 vardagar
With its mathematically rigorous presentation, this book is a detailed treatment of the approach from an inverse problems point of view. It is geared towards graduate students and researchers in applied mathematics and can serve as a text for graduate courses.
Häftad, Engelska, 2010
540 kr
Skickas inom 10-15 vardagar
This book is devoted to the study of variational methods in imaging. The presentation is mathematically rigorous and covers a detailed treatment of the approach from an inverse problems point of view. Many numerical examples accompany the theory throughout the text. It is geared towards graduate students and researchers in applied mathematics. Researchers in the area of imaging science will also find this book appealing. It can serve as a main text in courses in image processing or as a supplemental text for courses on regularization and inverse problems at the graduate level.
Inbunden, Engelska, 2027
1 302 kr
Kommande
This comprehensive volume develops deep learning methods for image reconstruction within the rigorous mathematical framework of regularization theory. The central thesis is that the principal deep learning approaches — data-consistent networks, learned regularization, and implicit non-variational regularization — are all convergent regularization methods: they produce stable reconstructions that converge to the exact solution as the noise level tends to zero. Each method is treated with full proofs and quantitative convergence rates. The theory covers both exact and approximate data consistency, including networks that learn corrections beyond the null space under regularization control. Numerical experiments on computed tomography, photoacoustic tomography, and inpainting illustrate the methods in full and limited data regimes.The book is aimed at master students, doctoral researchers, and scientists in inverse problems, medical imaging, and mathematical image reconstruction seeking a solid mathematical foundation.