Super-resolution image reconstruction has been one of the most active research areas in recent years. In this paper

a super-resolution solution is proposed to the problem of obtaining a high resolution image from several low resolution images that have been subsampled and displaced by different amounts of sub-pixel shifts. The method is based on the regularization technique

solving the constrained optimization by proposed iteration steps. At each iteration step

the regularization parameter is determined using the partially reconstructed image solved at the last step. The proposed algorithm is tested on synthetic images

and the reconstructed images are evaluated by a PSNR method. The results indicate that the proposed algorithm has considerable effectiveness in terms of both objective measurements and visual evaluation.