Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Deep Learning for Computational Imaging

Deep Learning for Computational Imaging

Heckel, Reinhard

Paperback

Technology & EngineeringGeneral Mathematics

ISBN10: 0198947186
ISBN13: 9780198947189
Publisher: Oxford University Press
Published: Aug 30 2025
Pages: 240
Weight: 0.95
Height: 0.90 Width: 6.50 Depth: 9.30
Language: English
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.

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