Staff Members - Benha University
Sahar A. El_Rahman, Mahmoud E. Allam, Hala A. Elqader, And Mazen Selim, ”Reconstruction Of High Resolution Image From A Set Of Blurred, Warped, Undersampled, And Noisy Measured Images”, © IEEE, ICENCO 2010, 6th International Computer Engineering Conference
6th International Computer Engineering Conference (ICENCO) • 2010
Publication Information
Authors
Sahar A. El_Rahman, Mahmoud E. Allam, Hala A. Elqader, And Mazen Selim
Keywords
Not Available
Journal
6th International Computer Engineering Conference (ICENCO)
Publisher
IEEE
Volume
Not Available
Issue
Not Available
Pages
Not Available
publication.type
International
Paper Link
Not Available
Supplementary Materials
Not Available
Abstract
This paper proposes an algorithm to reconstruct a
High Resolution (HR) image from a set of blurred, warped,
undersampled, and noisy measured images. The proposed
algorithm uses the affine block-based algorithm in the maximum
likelihood (ML) estimator. It is tested using synthetic images,
where the reconstructed image can be compared with its original.
A number of experiments were performed with the proposed
algorithm to evaluate its behavior before and after noise addition
and also compared with its behavior after noise removal. The
proposed system results show that the enhancement factor is
better after noise removal than in case of no noise is additive, and
show that PSNR difference is better in comparison with the
results of another system.
High Resolution (HR) image from a set of blurred, warped,
undersampled, and noisy measured images. The proposed
algorithm uses the affine block-based algorithm in the maximum
likelihood (ML) estimator. It is tested using synthetic images,
where the reconstructed image can be compared with its original.
A number of experiments were performed with the proposed
algorithm to evaluate its behavior before and after noise addition
and also compared with its behavior after noise removal. The
proposed system results show that the enhancement factor is
better after noise removal than in case of no noise is additive, and
show that PSNR difference is better in comparison with the
results of another system.