000			01685nam a22002057a 4500
005			20250501102021.0
008			250501b2021 |||||||| |||| 00| 0 eng d
020			_a9781108421614
082			_a006.6 ADC-B
100			_aAdcock, Ben
245			_aCompressive imaging : _bstructure, sampling, leaning / _cBen Adcock, Anders C. Hansen and Vegard Antun
260			_aIndia _bCambridge University Press _c2021
300			_a601p.
500			_aAccurate, 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
650			_aCompressed sensing (Telecommunication)
650			_aOptical data processing.
650			_aDigital images Deconvolution
650			_aImage compression
700			_aHansen, Anders C.
999			_c93463 _d93463