Motion Estimation using a Joint Optimisation of the Motion Vector Field and a Super-Resolution Reference Image

In many situations, interdependency between motion estimation and other estimation tasks is observable. This is for instance true in the area of super-resolution (SR). In order to successfully reconstruct a SR image, accurate motion vector fields are needed. On the other hand, one can only get accurate (subpixel) motion vectors, if there exist highly accurate higher-resolution reference images. Neglecting this interdependency may lead to poor estimation results for motion estimation as well as for the SR image. To address this problem, a new motion estimation scheme is presented that jointly optimises the motion vector field and a SR reference image. For this purpose and in order to attenuate aliasing and noise, which deteriorate the motion estimation, an observation model for the image acquisition process is applied and a maximum a posteriori (MAP) optimisation is performed, using Markov random field image models for regularisation. Results show that the new motion estimator provides more accurate motion vector fields than classical block motion estimation techniques. The joint optimisation scheme yields an estimate of the motion vector field as well as a SR image.