In traditional digital image restoration, the blurring process of the optic is assumed known. Many previous research efforts have been trying to reconstruct the degraded image or video sequence with either partially known or totally unknown point spread function (PSF) of the optical lens, which is commonly called the blind deconvolution problem. Many methods have been proposed in the application to image restoration. However, little work has been done in the super-resolution scenario. In this paper, we propose a generalized framework of regularized image/video iterative blind deconvolution / super-resolution (IBD-SR) algorithm, using some information from the more matured blind deconvolution techniques from image restoration. The initial estimates for the image restoration help the iterative image/video super-resolution algorithm converge faster and be stable. Experimental results are presented and conclusions are drawn.
A regularization framework for joint blur estimation and super-resolution of video sequences
IEEE International Conference on Image Processing 2005 ; 3 ; III-329
2005-01-01
198045 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
A Regularization Framework for Joint Blur Estimation and Super-Resolution of Video Sequences
British Library Conference Proceedings | 2005
|Double Regularized Bayesian Estimation for Blur Identification in Video Sequences
Springer Verlag | 2006
|Double Regularized Bayesian Estimation for Blur Identification in Video Sequences
British Library Conference Proceedings | 2006
|British Library Online Contents | 2004
|Simultaneous Super-Resolution for Video Sequences
British Library Conference Proceedings | 2005
|