Description

Fully updated throughout and with several new chapters, this second edition of Introduction to Inverse Problems in Imaging guides advanced undergraduate and graduate students in physics, computer science, mathematics and engineering through the principles of linear inverse problems, in addition to methods of their approximate solution and their practical applications in imaging.

This second edition contains new chapters on edge-preserving and sparsity-enforcing regularization in addition to maximum likelihood methods and Bayesian regularization for Poisson data.

The level of mathematical treatment is kept as low as possible to make the book suitable for a wide range of students from different backgrounds, with readers needing just a rudimentary understanding of analysis, geometry, linear algebra, probability theory, and Fourier analysis.

The authors concentrate on presenting easily implementable and fast solution algorithms, and this second edition

Introduction to Inverse Problems in Imaging

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    £45.99

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    Order before 4pm tomorrow for delivery by Wed 12 Aug 2026.

    A Paperback by Christine De Mol, P. Boccacci, Christine De Mol

    15 in stock

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      Publisher: Taylor & Francis Ltd
      Publication Date: Publication Date: 1/29/2024 12:00:00 AM
      ISBN13: 9780367467869, 978-0367467869
      ISBN10: 0367467860
      Also in:
      Medical imaging Optical physics Applied optics

      Description

      Fully updated throughout and with several new chapters, this second edition of Introduction to Inverse Problems in Imaging guides advanced undergraduate and graduate students in physics, computer science, mathematics and engineering through the principles of linear inverse problems, in addition to methods of their approximate solution and their practical applications in imaging.

      This second edition contains new chapters on edge-preserving and sparsity-enforcing regularization in addition to maximum likelihood methods and Bayesian regularization for Poisson data.

      The level of mathematical treatment is kept as low as possible to make the book suitable for a wide range of students from different backgrounds, with readers needing just a rudimentary understanding of analysis, geometry, linear algebra, probability theory, and Fourier analysis.

      The authors concentrate on presenting easily implementable and fast solution algorithms, and this second edition

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