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020 _a9781107415805 (ebook)
020 _z9781107058545 (hardback)
035 _a(UkCbUP)CR9781107415805
039 9 _y03-08-2017
_zhafiz
040 _aUkCbUP
_beng
_erda
_cUkCbUP
050 0 0 _aQA274.23
_b.U57 2014
082 0 0 _a519.2/3
_223
100 1 _aUnser, Michael,
_eauthor.
245 1 3 _aAn Introduction to Sparse Stochastic Processes /
_cMichael Unser, Pouya D. Tafti.
264 1 _aCambridge :
_bCambridge University Press,
_c2014.
300 _a1 online resource (384 pages) :
_bdigital, PDF file(s).
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
500 _aTitle from publisher's bibliographic system (viewed on 07 Mar 2017).
520 _aProviding a novel approach to sparsity, this comprehensive book presents the theory of stochastic processes that are ruled by linear stochastic differential equations, and that admit a parsimonious representation in a matched wavelet-like basis. Two key themes are the statistical property of infinite divisibility, which leads to two distinct types of behaviour - Gaussian and sparse - and the structural link between linear stochastic processes and spline functions, which is exploited to simplify the mathematical analysis. The core of the book is devoted to investigating sparse processes, including a complete description of their transform-domain statistics. The final part develops practical signal-processing algorithms that are based on these models, with special emphasis on biomedical image reconstruction. This is an ideal reference for graduate students and researchers with an interest in signal/image processing, compressed sensing, approximation theory, machine learning, or statistics.
650 0 _aStochastic differential equations
650 0 _aRandom fields
650 0 _aGaussian processes
700 1 _aTafti, Pouya D.,
_eauthor.
776 0 8 _iPrint version:
_z9781107058545
856 4 0 _uhttps://doi.org/10.1017/CBO9781107415805
907 _a.b16441357
_b2019-11-14
_c2019-11-12
942 _n0
914 _avtls003618416
998 _anone
_b2017-08-03
_cm
_da
_feng
_genk
_y0
_z.b16441357
999 _c617351
_d617351