Are Registration Errors Always Bad for Super-Resolution?

The super-resolution reconstruction (SRR) of images is an ill posed problem. Traditionally it is treated as a regularized minimization problem. Moreover, one of the major problems concerning SRR is its dependence on an accurate registration. In this work we show that a certain amount of registration error may. in fact, be beneficial for the performance of the least mean square SRR (LMS-SRR) adaptive algorithm. In these cases, the regularization term can be avoided and computational cost is reduced, an important advantage in real-time SRR applications.