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Spectrum analysis --- Deconvolution.

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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.

Image compression. --- Digital images --- Deconvolution.

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Estimation theory. --- Seismic reflection method --- Signal processing. --- Deconvolution.

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Seismic reflection method. --- Seismic reflection method --- Seismic profiling --- Deconvolution.

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Theoretical spectroscopy. Spectroscopic techniques --- fysicochemie --- Absorption spectra --- -535 --- Selective absorption --- Exciton theory --- Molecular spectroscopy --- Spectrum analysis --- Deconvolution --- Optics --- 535 Optics --- 535 --- Deconvolution of absorption spectra