Deep Learning for Computational Imaging

Name: Deep Learning for Computational Imaging
Brand: Oxford University Press
SKU: 9780198947189
Price: 54.2 EUR
Availability: InStock

Автор: Heckel, Prof Reinhard (Professor of Machine Learning (Tenured Associate Professor), Professor of Machine Learning (Tenured Associate Professor), Technical University of Munich)

Език:

Корици: С меки корици

Издател: Oxford University Press

Наличност: Външен склад

Изпращаме след 9-15 дни

54.20 € 106.00 лв

Computational techniques for image reconstruction problems enable imaging technologies including hig...

Информация за книгата

Автор

Heckel, Prof Reinhard (Professor of Machine Learning (Tenured Associate Professor), Professor of Machine Learning (Tenured Associate Professor), Technical University of Munich)

Език

Английски език

Корици

Книга - С меки корици

Издадена

2025

страници

224

EAN

9780198947189

ISBN

0198947186

Enbook ID

46878210

Издател

Oxford University Press

Теглоt

408

Размери

156 x 234

Пълно описание

Computational techniques for image reconstruction problems enable imaging technologies including high-resolution microscopy, astronomy and seismology, computed tomography, and magnetic resonance imaging. Until recently, methods for solving such inverse problems were derived by experts without any learning. Now, the best performing image reconstruction methods are based on deep learning. This textbook gives the first comprehensive introduction to deep learning based image reconstruction methods. This book first introduces important inverse problems in imaging, including denoising and reconstructing an image from few and noisy measurements, and explains what makes those problems hard and interesting. Then, the book briefly discusses traditional optimization and sparsity based reconstruction methods, as well as optimization techniques as a basis for training and deriving deep neural networks for image reconstruction. The main part of the book is about how to solve image reconstruction problems with deep learning techniques: The book first disuses supervised deep learning approaches that map a measurement to an image as well as network architectures for imaging including convolutional neural networks and transformers. Then, reconstruction approaches based on generative models such as variational autoencoders and diffusion models are discussed, and how un-trained neural networks and implicit neural representations enable signal and image reconstruction. The book ends with a discussion on the robustness of deep learning based reconstruction as well as a discussion on the important topic of evaluating models and datasets, which are a critical ingredient of deep learning based imaging.

Може също да ви хареса

Клиенти, които купиха тази книга, купиха също

Най-често търсени

Categories

Authors

Publishers

Продукти

Продукти

Най-често търсени

Categories

Authors

Publishers

Продукти

Продукти

Deep Learning for Computational Imaging

Информация за книгата

Пълно описание

Може също да ви хареса

Клиенти, които купиха тази книга, купиха също