One of the challenging tasks in today image processing is image registration. Image registration is inevitable whenever images taken for example at different times or from different perspectives need to be compared or to be integrated. Typically, the location of corresponding points in the different views of one object or even of different objects is distorted. For example, motion or different properties of the underlying optical systems (MR, CT) are responsible for the distortion. Thus, a basic problem is to find a meaningful spatial transformation of a given image, such that the transformed image becomes similar to a given second one. Typically, the transformation is computed by minimizing a suitable similarity measure. For many applications it is also desirable to guide the registration by additional information, like the locations of outstanding points. In this note, be present a general variational based approach for image registration which allows the choice of a user supplied similarity measure and a user supplied regularizer as well as the integration of external knowledge, like, for example, the location of outstanding points.