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001 978-3-319-10485-0
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007 cr nn 008mamaa
008 140922s2014 gw | s |||| 0|eng d
020 _a9783319104850
_9978-3-319-10485-0
024 7 _a10.1007/978-3-319-10485-0
_2doi
050 4 _aTA1637-1638
050 4 _aTA1634
072 7 _aUYT
_2bicssc
072 7 _aUYQV
_2bicssc
072 7 _aCOM012000
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072 7 _aCOM016000
_2bisacsh
082 0 4 _a006.6
_223
082 0 4 _a006.37
_223
100 1 _aChaudhuri, Subhasis.
_eauthor.
245 1 0 _aBlind Image Deconvolution
_h[electronic resource] :
_bMethods and Convergence /
_cby Subhasis Chaudhuri, Rajbabu Velmurugan, Renu Rameshan.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aXV, 151 p. 33 illus., 16 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aIntroduction -- Mathematical Background -- Blind Deconvolution Methods: A Review -- MAP Estimation: When Does it Work? -- Convergence Analysis in Fourier Domain -- Spatial Domain Convergence Analysis -- Sparsity-based Blind Deconvolution -- Conclusions and Future Research Directions.
520 _aBlind deconvolution is a classical image processing problem which has been investigated by a large number of researchers over the last four decades. The purpose of this monograph is not to propose yet another method for blind image restoration. Rather the basic issue of deconvolvability has been explored from a theoretical view point. Some authors claim very good results while quite a few claim that blind restoration does not work. The authors clearly detail when such methods are expected to work and when they will not. In order to avoid the assumptions needed for convergence analysis in the Fourier domain, the authors use a general method of convergence analysis used for alternate minimization based on three point and four point properties of the points in the image space. The authors prove that all points in the image space satisfy the three point property and also derive the conditions under which four point property is satisfied. This provides the conditions under which alternate minimization for blind deconvolution converges with a quadratic prior. Since the convergence properties depend on the chosen priors, one should design priors that avoid trivial solutions. Hence, a sparsity based solution is also provided for blind deconvolution, by using image priors having a cost that increases with the amount of blur, which is another way to prevent trivial solutions in joint estimation. This book will be a highly useful resource to the researchers and academicians in the specific area of blind deconvolution.
650 0 _aComputer science.
650 0 _aImage processing.
650 1 4 _aComputer Science.
650 2 4 _aImage Processing and Computer Vision.
650 2 4 _aSignal, Image and Speech Processing.
700 1 _aVelmurugan, Rajbabu.
_eauthor.
700 1 _aRameshan, Renu.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319104843
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-10485-0
912 _aZDB-2-SCS
942 _cEBK
999 _c56030
_d56030