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Generalized-cross-validation estimation of the regularization parameters of the subbands in wavelet domain regularized image restoration

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dc.contributor.author Stephanakis Ioannis, M en
dc.contributor.author Kollias, Stefanos en
dc.date.accessioned 2014-03-01T02:41:33Z
dc.date.available 2014-03-01T02:41:33Z
dc.date.issued 1998 en
dc.identifier.issn 10586393 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/30521
dc.subject Cost Function en
dc.subject Generalized Cross Validation en
dc.subject Image Restoration en
dc.subject.other Computational methods en
dc.subject.other Image analysis en
dc.subject.other Image quality en
dc.subject.other Parameter estimation en
dc.subject.other Signal filtering and prediction en
dc.subject.other Generalized-cross-validation (GCV) methods en
dc.subject.other Image reconstruction en
dc.title Generalized-cross-validation estimation of the regularization parameters of the subbands in wavelet domain regularized image restoration en
heal.type conferenceItem en
heal.identifier.primary 10.1109/ACSSC.1998.751400 en
heal.identifier.secondary http://dx.doi.org/10.1109/ACSSC.1998.751400 en
heal.publicationDate 1998 en
heal.abstract A model of regularized image restoration in the wavelet domain is presented in this paper. Separable 2-D wavelets, constructed as the tensorial product of 1-D Daubechies wavelets of order four (N = 4), replace the conventional smoothing filter in the regularized image restoration problem. The regularized solution is computed by minimizing a cost functional which depends upon four regularization parameters (λLL, λHL, λLH and λHH) corresponding to different subbands. The relationship between the remaining restoration noise and the restored image is given in closed form. A direct solution of the restoration problem is then proposed based upon this relationship. The Generalized-Cross-Validation (GCV) method is applied to estimate the optimal values of the restoration parameters with no prior assumption regarding the original image. Experimental results obtained from the solution of the regularization equation indicate that the proposed method is superior compared to conventional regularized restoration using the Laplacian as smoothing filter. en
heal.publisher IEEE Comp Soc, Los Alamitos, CA, United States en
heal.journalName Conference Record of the Asilomar Conference on Signals, Systems and Computers en
dc.identifier.doi 10.1109/ACSSC.1998.751400 en
dc.identifier.volume 2 en
dc.identifier.spage 938 en
dc.identifier.epage 940 en


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