Blind Image Deconvolution: Methods and Convergence
Autor Subhasis Chaudhuri, Rajbabu Velmurugan, Renu Rameshanen Limba Engleză Hardback – 7 oct 2014
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.
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Specificații
ISBN-13: 9783319104843
ISBN-10: 3319104845
Pagini: 151
Ilustrații: XV, 151 p. 33 illus., 16 illus. in color.
Dimensiuni: 155 x 235 x 17 mm
Greutate: 0.41 kg
Ediția:2014
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland
ISBN-10: 3319104845
Pagini: 151
Ilustrații: XV, 151 p. 33 illus., 16 illus. in color.
Dimensiuni: 155 x 235 x 17 mm
Greutate: 0.41 kg
Ediția:2014
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland
Public țintă
ResearchCuprins
Introduction.- 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.
Caracteristici
First ever dedicated text on convergence issues in blind deconvolution Discusses the conditions under which blind deconvolution works Presents the nature of image priors which prevents trivial solutions, all of which allow practitioners to design image restoration algorithms Includes supplementary material: sn.pub/extras