Machine learning based supper-resolution algorithm robust to registration errors

In this work, a novel two phase approach is proposed for robust super-resolution in the presence of registration errors and outliers. In the first phase machine learning method is used to create a weight matrix for every LR image indicating the presence of registration errors. In the second phase, super-resolution is performed using all of the LR images and the associated weight matrices, creating an image which is free of error artifacts.