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

Stephanakis Ioannis, M; Kollias, Stefanos

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