One of the major issues in recovering a high-resolution image from a sequence of low-resolution observations is the accuracy of the motion information. In most of the work in the literature, the motion information is assumed to be known with high accuracy. This is very often not the case, and therefore the accuracy of high-resolution image reconstruction suffers substantially, since it greatly depends on the motion information. To address these issues in this paper, we propose a high-resolution image reconstruction algorithm that reduces the distortion in the reconstructed high-resolution image due to the inaccuracy of the estimated motion. Towards this task, we analyze the reconstruction noise generated by the inaccurate motion information. Based on this analysis, we propose a new regularization functional and derive a sufficient condition for the convergence of the resulting iterative reconstruction algorithm. The proposed algorithm requires no prior information about the original image or the inaccuracy of the motion information. Experimental results illustrate the benefit of the proposed method when compared to conventional high-resolution image reconstruction methods in terms of both objective measurements and subjective evaluation.