LEADER 04193nam 22006735 450 001 9910298975503321 005 20200701061459.0 010 $a3-319-10485-3 024 7 $a10.1007/978-3-319-10485-0 035 $a(CKB)3710000000248958 035 $a(EBL)1965346 035 $a(OCoLC)893678333 035 $a(SSID)ssj0001353810 035 $a(PQKBManifestationID)11730290 035 $a(PQKBTitleCode)TC0001353810 035 $a(PQKBWorkID)11322568 035 $a(PQKB)11003387 035 $a(MiAaPQ)EBC1965346 035 $a(DE-He213)978-3-319-10485-0 035 $a(PPN)181348608 035 $a(EXLCZ)993710000000248958 100 $a20140922d2014 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBlind Image Deconvolution $eMethods and Convergence /$fby Subhasis Chaudhuri, Rajbabu Velmurugan, Renu Rameshan 205 $a1st ed. 2014. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2014. 215 $a1 online resource (162 p.) 300 $aDescription based upon print version of record. 311 $a3-319-10484-5 320 $aIncludes bibliographical references and index. 327 $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. 330 $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. 606 $aOptical data processing 606 $aSignal processing 606 $aImage processing 606 $aSpeech processing systems 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051 615 0$aOptical data processing. 615 0$aSignal processing. 615 0$aImage processing. 615 0$aSpeech processing systems. 615 14$aImage Processing and Computer Vision. 615 24$aSignal, Image and Speech Processing. 676 $a004 676 $a006.37 676 $a006.6 676 $a621.382 700 $aChaudhuri$b Subhasis$4aut$4http://id.loc.gov/vocabulary/relators/aut$0846530 702 $aVelmurugan$b Rajbabu$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aRameshan$b Renu$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910298975503321 996 $aBlind Image Deconvolution$92060911 997 $aUNINA