Reconstruction of High Resolution image from a set of blurred, warped, undersampled, and noisy measured images

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.