In the last two decades, many papers have been published, proposing a variety methods of multi-frame resolution enhancement. These methods are usually very sensitive to their assumed model of data and noise, which limits their utility. This paper reviews some of these methods and addresses their shortcomings. We propose a different implementation using L 1 norm minimization and robust regularization to deal with different data and noise models. This computationally inexpensive method is robust to errors in motion and blur estimation, and results in sharp edges. Simulation results confirm the effectiveness of our method and demonstrate its superiority to other robust super-resolution methods.