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  3. Compressive Imaging: Structure, Sampling, Learning
  • Cited by 30
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    • Publisher:
      Cambridge University Press
      Publication date:
      July 2021
      September 2021
      ISBN:
      9781108377447
      9781108421614
      DOI:
      https://doi.org/10.1017/9781108377447
      Dimensions:
      (244 x 170 mm)
      Weight & Pages:
      1.34kg, 614 Pages
      Dimensions:
      Weight & Pages:
      • Subjects:
      Mathematics, Computational Science, Engineering, Image Processing and Machine Vision
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    Subjects:
    Mathematics, Computational Science, Engineering, Image Processing and Machine Vision
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    Book description

    Accurate, robust and fast image reconstruction is a critical task in many scientific, industrial and medical applications. Over the last decade, image reconstruction has been revolutionized by the rise of compressive imaging. It has fundamentally changed the way modern image reconstruction is performed. This in-depth treatment of the subject commences with a practical introduction to compressive imaging, supplemented with examples and downloadable code, intended for readers without extensive background in the subject. Next, it introduces core topics in compressive imaging – including compressed sensing, wavelets and optimization – in a concise yet rigorous way, before providing a detailed treatment of the mathematics of compressive imaging. The final part is devoted to recent trends in compressive imaging: deep learning and neural networks. With an eye to the next decade of imaging research, and using both empirical and mathematical insights, it examines the potential benefits and the pitfalls of these latest approaches.

    Awards

    Finalist, 2022 PROSE Award for Computing and Information Sciences

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