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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Compressive Imaging: Structure, Sampling, Learning (Hardcover)
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Compressive Imaging: Structure, Sampling, Learning (Hardcover)
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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.
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