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000711798 020__ $$a9783319104850$$qelectronic book
000711798 020__ $$a3319104853$$qelectronic book
000711798 020__ $$z9783319104843
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000711798 0247_ $$a10.1007/978-3-319-10485-0$$2doi
000711798 035__ $$aSP(OCoLC)ocn892517178
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000711798 049__ $$aISEA
000711798 050_4 $$aTA1632
000711798 08204 $$a006.6$$223
000711798 1001_ $$aChaudhuri, Subhasis,$$eauthor.
000711798 24510 $$aBlind image deconvolution$$h[electronic resource] :$$bmethods and convergence /$$cSubhasis Chaudhuri, Rajbabu Velmurugan, Renu Rameshan.
000711798 264_1 $$aCham :$$bSpringer,$$c2014.
000711798 300__ $$a1 online resource (xv, 151 pages) :$$billustrations (some color)
000711798 336__ $$atext$$btxt$$2rdacontent
000711798 337__ $$acomputer$$bc$$2rdamedia
000711798 338__ $$aonline resource$$bcr$$2rdacarrier
000711798 504__ $$aIncludes bibliographical references and index.
000711798 5050_ $$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.
000711798 506__ $$aAccess limited to authorized users.
000711798 520__ $$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.
000711798 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 8, 2014).
000711798 650_0 $$aImage processing.
000711798 650_0 $$aImage processing$$xMathematics.
000711798 7001_ $$aVelmurugan, Rajbabu,$$eauthor.
000711798 7001_ $$aRameshan, Renu,$$eauthor.
000711798 77608 $$iPrint version:$$z9783319104843
000711798 85280 $$bebk$$hSpringerLink
000711798 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://dx.doi.org/10.1007/978-3-319-10485-0$$zOnline Access
000711798 909CO $$ooai:library.usi.edu:711798$$pGLOBAL_SET
000711798 980__ $$aEBOOK
000711798 980__ $$aBIB
000711798 982__ $$aEbook
000711798 983__ $$aOnline
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